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Municipal differences in population size, vaccination coverage and mortality rate (example for week 50 of 2021). A subset of all 340 municipalities' names appear on the left.
Source publication
We analyse the relation between covid-19 vaccinations and all-cause-mortality in N=340 Dutch municipalities (17.3M people, ~99% of population), during the entire pandemic period. We do not use covid-19-attributed mortality, mortality predictions and excess mortality, thereby bypassing the ambiguities of case-identification and mortality-modeling. M...
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Purpose:
The Omicron subvariant BA.1 of SARS-CoV-2 was first detected in November 2021 and quickly spread worldwide, displacing the Delta variant. In this work, a characterization of the spread of this variant in Mexico is presented.
Methods:
The time to fixation of BA.1, the diversity of Delta sublineages, the population density, and the level...
Citations
... Recently, several such excess mortality analyses have been made that are independent of existing national baselines and do not aim at forecasting, attempting to reduce subjectivity. In [Red1], a geographical-differential analysis was used, while [Red2,Red3] used a temporal-differential analysis effectively defining a baseline via a period around a moment in the past. In [Mee], a fixed baseline was estimated and used within the same analysis time frame. ...
... Time-since-full-vaccination (10 months total) If above VE's bare any relation with reality, one would expect that my results would show at least something remotely similar. Each of my four studies ([Red1,Red2,Red3] and the current, covering very different model approaches) finds the opposite: more vaccinations mean more mortality. The only similarity with Figure 28 is located at the end. ...
A new mortality model is presented for more objective quantification and localization in time of excess mortality.
The model is just one equation, M = W*atT, with source data of mortality and temperature. Parameters represent trends over decades, seasonal/weekly variability, delay between cause and death, frailty regulating delay between excess and deficit, lifetime lost/saved (LTL/LTS), and natural mortality variability. Pandemic excesses are examined by integrating determinants of positive covid tests and vaccinations. Measured parameters are covid Case Fatality Rate (CFR), Vaccine-dose Fatality Rate (VFR), Vaccine Effectivity (VE) against covid mortality, and LTL/LTS by covid and vaccination.
Experiments involve 10 EU countries (344M people) and age-stratified datasets in The Netherlands over years 2000-2023. Predictions and excess measurements by the proposed model are fivefold more accurate than those of the national baseline. Covid is found to target much frailer people than natural causes do, and vaccine fatal events target younger and healthier people. Measured lifetime lost to vaccinations is of the same order as lifetime saved: Germany has LTL 450-4600ky (kiloyear), Netherlands 50-500ky.
The model is very different from the state of the art and many options for further research are outlined. A disturbing relation is found between vaccinations and the persisting excess mortality among the young and healthy population.
Keywords: Mortality, temperature, frailty, pandemic, excess, deficit, covid, vaccine, QALY, lifetime
... In other words, vaccines appear to have contributed (directly or indirectly) to an increase in other causes of death (than covid), in young populations. Those previous conclusions are now consistent with statistical results brought by a dozen of preprints and papers [1,3,5,16,19,20,28,30,33,34,38]. However, they are seemingly in contradiction with several major papers such as [26,42] as well as [6,21,22,41] that defend (based on all-cause mortality) a positive benet/risk balance. ...
The question whether Covid-19 vaccination campaigns could have had an immediate negative impact on excess deaths continues to be debated two years later, in particular in the less than 45 years old. When the age-stratified (anonymized) vaccination status of deceased will be publicly available, the debate should come to an end. In the meantime, this paper provides three new statistical analyses that further shed light on the matter. Two of them connect the temporality of all-cause mortality data with injection data. Another analysis, using internet search trends, investigates possible alternative explanations. We deem that taken together, as it is done in this paper, those three analyses reinforce our previous conclusions suggesting caution when it comes to vaccinating/boosting young European populations.
A preprint is available here:
https://hdl.handle.net/2268/314674
... Several other studies have implicated COVID-19 vaccination in increased all-cause mortality. These studies have used different methods, countries, age groups, and time periods covering different COVID-19 variants, different vaccine histories, and time following vaccination [157][158][159][160][161][162][163]. A common theme is the involvement of mRNA vaccines, and poor outcomes in children and young adults. ...
All vaccines exhibit both specific and non-specific effects. The specific effects are measured by the efficacy against the target pathogen, while the non-specific effects can be detected by the change in all-cause mortality. All-cause mortality data (gender, age band, vaccination history, month of death) between January 2021 and May 2022 was compiled by the Office for National Statistics. COVID-19 vaccination gave good protection on many occasions but less so for younger ages. Each gender and age group shows its own unique vaccination benefit/disbenefit time profile. Individuals are free to make vaccination decisions. For example, women aged 18-39 show a cohort who do not progress beyond the first or second dose. The all-cause mortality outcomes for the Omicron variant showed a very poor response to vaccination with 70% of sex/age/vaccination stage/month combinations increasing all-cause mortality, probably due to unfavorable antigenic distance between the first-generation vaccines and this variant, and additional non-specific effects. The all-cause mortality outcomes of COVID-19 vaccination is far more nuanced than have been widely appreciated, and virus vector appear better than the mRNA vaccines in this specific respect. The latter are seemingly more likely to increase all-cause mortality especially in younger age groups. An extensive discussion/literature review is included to provide potential explanations for the observed unexpected vaccine effects. Full text and Supplementary material at: https://www.preprints.org/manuscript/202304.0248/v1
Note that we are about to submit a version of this paper looking at the effects on non-COVID-19 all-cause mortality (NCACM).After that we aim to return to the all-cause mortality paper.
... In The Netherlands (population ~18M), excess mortality rates went up to ~80 people/day (excess ~20%) at end of 2022, before rising even more due to influenza. Based on the sparse publicly available Dutch data on mortality and vaccination, excess mortality was found to correlate positively with vaccination on long-term [Re1], and short-term [Mee,Sch,Re2]. Figure 1 illustrates this correlation for weekly 4th/5th vaccination campaign doses and all-cause mortality. ...
This report investigates short-term causal vaccine-mortality interactions during booster campaigns in 2022 in 30 European countries (population ~530M). An infection-vaccination-mortality model is introduced with causal aspects of repeatability, random chance, temporal order and confounding. The model is simple, has few or even zero prior model parameters and is unbiased in causal mechanisms and strengths. Confounders are taken into account explicitly of mortality-caused fear incentivizing vaccinations and four related to covid infections, and generically for all long-term confounding. Bayesian probabilities quantify all interactions, and from observed weekly administered vaccine doses and all-cause mortality, mortality on short-term caused by a vaccination dose is estimated as Vaccine Fatality Ratio (VFR).
VFR results are 0.13% (0.05%-0.21%, 95% confidence interval) in The Netherlands and 0.35% (0.15%-0.55%) in Europe, subtantially transcending covid IFR. Additionally, sewer-viral-particle experiments suggested vaccination induces covid-infections and/or reactivates latent viral reservoirs.
The evidence of a causal relationship from vaccination to both infection and mortality is a very strong alarm signal to immediately stop current mass vaccination programmes.
This report investigates the natural variability of mortality, which determines the thin line between excesses and normal variation within expectations. I propose a model for mortality’s weekly variability based on a Poisson model, driven by potential non-stationary influences that act nation-wide and fast, on time scales of a week.
Results reveal the presence of a significant amount of non-stationary influences that add to mortality’s weekly variability, with a magnitude of 3%±1% (1% - 5% with 95% confidence) times baseline mortality, on top of the standard (Poisson) variability. This additional variability is consistently found across 30 European countries (462M people) during 2017-2019. A long-term analysis in The Netherlands (17M people) reveals the same variability between 2010-2019, with substantial increase since 2020 to approx. 5%. Mortality variance may thus very well itself be used as event indicator when variability is higher than expected.
The findings in this report are relevant for all models of mortality and its variability in general, and in particular for developments towards better excess mortality measurements. The additional variability found scales with both baseline mortality and population size in a different way compared to Poisson variability. For a typical mortality baseline of approx. 0.02% per week, the additional variability becomes dominant in populations above approximately 5M people.
A number of causes for the variability found are suggested, of which the most promising, temperature, will be investigated in a follow-up to this report.
This report proposes a new and simple method to measure vaccination-mortality correlation, test for causality, and if passed, obtain Vaccine Fatality Ratio (VFR), in the short-term of 0-4 weeks after vaccination. Only data of weekly administered number of doses and weekly all-cause-deaths are required, without reference to e.g. mortality prognoses, covid-labeled deaths or excess mortality.
This report presents evidence of a short-term causal relation from 4th-dose covid-vaccination to mortality in The Netherlands, with a VFR of 0.18% corresponding to ca. 4400 vaccine-related deaths, in the order of magnitudes of covid IFR and observed excess mortality.
