Mortality Prediction by Time, Temperature and Frailty applied to Pandemic Excesses by Covid and Vaccines
Preprints and early-stage research may not have been peer reviewed yet.
Abstract and Figures
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
Figures - uploaded by André Redert
Author content
All figure content in this area was uploaded by André Redert
Content may be subject to copyright.
ResearchGate has not been able to resolve any citations for this publication.
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.
Background
This study estimates the burden of COVID-19 on mortality in Germany. It is expected that many people have died because of the new COVID-19 virus who otherwise would not have died. Estimating the burden of the COVID-19 pandemic on mortality by the number of officially reported COVID-19-related deaths has been proven to be difficult due to several reasons. Because of this, a better approach, which has been used in many studies, is to estimate the burden of the COVID-19 pandemic by calculating the excess mortality for the pandemic years. An advantage of such an approach is that additional negative impacts of a pandemic on mortality are covered as well, such as a possible pandemic-induced strain on the healthcare system.
Methods
To calculate the excess mortality in Germany for the pandemic years 2020 to 2022, we compare the reported number of all-cause deaths (i.e., the number of deaths independently of underlying causes) with the number of statistically expected all-cause deaths. For this, the state-of-the-art method of actuarial science, based on population tables, life tables, and longevity trends, is used to estimate the expected number of all-cause deaths from 2020 to 2022 if there had been no pandemic.
Results
The results show that the observed number of deaths in 2020 was close to the expected number with respect to the empirical standard deviation; approximately 4,000 excess deaths occurred. By contrast, in 2021, the observed number of deaths was two empirical standard deviations above the expected number and even more than four times the empirical standard deviation in 2022. In total, the number of excess deaths in the year 2021 is about 34,000 and in 2022 about 66,000 deaths, yielding a cumulated 100,000 excess deaths in both years. The high excess mortality in 2021 and 2022 was mainly due to an increase in deaths in the age groups between 15 and 79 years and started to accumulate only from April 2021 onward. A similar mortality pattern was observed for stillbirths with an increase of about 9.4% in the second quarter and 19.4% in the fourth quarter of the year 2021 compared to previous years.
Conclusions
These findings indicate that something must have happened in spring 2021 that led to a sudden and sustained increase in mortality, although no such effects on mortality had been observed during the early COVID-19 pandemic so far. Possible influencing factors are explored in the discussion.
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.
In this note we study the likelihood of short-term effects on all cause mortality, based on epidemiological data, in the third booster campaign in The Netherlands, which was among the 65+ age cohort. Since individual data in The Netherlands is not available, we used weekly aggregate data published by the Dutch government on vaccination volumes and all cause mortality. We consider a class of models with only two parameters: weekly mortality rates before and after vaccination. We only consider combinations of parameters which lead to the same number of (observed) expected all cause mortality, so that any difference is solely due to temporal patterns. Within this class of models, we varied the models from live-saving to live-threatening. The model that assumes vaccination caused 1250 deaths during the campaign seems to explain the data best. We also performed a goodness-of-fit analysis for this particular choice, and conclude that it fits the data well. Our findings deviate from certain other findings in the literature , where other campaigns in other countries and time-frames were investigated; beneficial or adverse effects can apparently possibly be (very) different for different vaccination campaigns, depending on the circumstances.
Background:
The rapid development of COVID-19 vaccines, combined with a high number of adverse event reports, have led to concerns over possible mechanisms of injury including systemic lipid nanoparticle (LNP) and mRNA distribution, Spike protein-associated tissue damage, thrombogenicity, immune system dysfunction, and carcinogenicity. The aim of this systematic review is to investigate possible causal links between COVID-19 vaccine administration and death using autopsies and post-mortem analysis.
Methods:
We searched PubMed and ScienceDirect for all published autopsy and necropsy reports relating to COVID-19 vaccination up until May 18th, 2023. All autopsy and necropsy studies that included COVID-19 vaccination as an antecedent exposure were included. Because the state of knowledge has advanced since the time of the original publications, three physicians independently reviewed each case and adjudicated whether or not COVID-19 vaccination was the direct cause or contributed significantly to death.
Results:
We initially identified 678 studies and, after screening for our inclusion criteria, included 44 papers that contained 325 autopsy cases and one necropsy case. The mean age of death was 70.4 years. The most implicated organ system among cases was the cardiovascular (49%), followed by hematological (17%), respiratory (11%), and multiple organ systems (7%). Three or more organ systems were affected in 21 cases. The mean time from vaccination to death was 14.3 days. Most deaths occurred within a week from last vaccine administration. A total of 240 deaths (73.9%) were independently adjudicated as directly due to or significantly contributed to by COVID-19 vaccination, of which the primary causes of death include sudden cardiac death (35%), pulmonary embolism (12.5%), myocardial infarction (12%), VITT (7.9%), myocarditis (7.1%), multisystem inflammatory syndrome (4.6%), and cerebral hemorrhage (3.8%).
Conclusions:
The consistency seen among cases in this review with known COVID-19 vaccine mechanisms of injury and death, coupled with autopsy confirmation by physician adjudication, suggests there is a high likelihood of a causal link between COVID-19 vaccines and death. Further urgent investigation is required for the purpose of clarifying our findings.
We examined the possible non-specific effects of novel mRNA- and adenovirus-vector COVID-19 vaccines by reviewing the randomized control trials (RCTs) of mRNA and adenovirus-vector COVID-19 vaccines. We calculated mortality risk ratios (RRs) for mRNA COVID-19 vaccines vs. placebo recipients and compared them with the RR for adenovirus-vector COVID-19 vaccine recipients vs. controls. The RR for overall mortality of mRNA vaccines vs. placebo was 1.03 (95% confidence interval [CI]: 0.63–1.71). In the adenovirus-vector vaccine RCTs, the RR for overall mortality was 0.37 (0.19–0.70). The two vaccine types differed significantly with respect to impact on overall mortality (p = 0.015). The RCTs of COVID-19 vaccines were unblinded rapidly, and controls were vaccinated. The results may therefore not be representative of the long-term effects. However, the data argue for performing RCTs of mRNA and adenovirus-vector vaccines head-to-head comparing long-term effects on overall mortality.
Background:
Synthesising evidence on the long-term vaccine effectiveness of COVID-19 vaccines (BNT162b2 [Pfizer-BioNTech], mRNA-1273 [Moderna], ChAdOx1 nCoV-19 [AZD1222; Oxford-AstraZeneca], and Ad26.COV2.S [Janssen]) against infections, hospitalisations, and mortality is crucial to making evidence-based pandemic policy decisions.
Methods:
In this rapid living systematic evidence synthesis and meta-analysis, we searched EMBASE and the US National Institutes of Health's iSearch COVID-19 Portfolio, supplemented by manual searches of COVID-19-specific sources, until Dec 1, 2022, for studies that reported vaccine effectiveness immediately and at least 112 days after a primary vaccine series or at least 84 days after a booster dose. Single reviewers assessed titles, abstracts, and full-text articles, and extracted data, with a second reviewer verifying included studies. The primary outcomes were vaccine effectiveness against SARS-CoV-2 infections, hospitalisations, and mortality, which were assessed using three-level meta-analytic models. This study is registered with the National Collaborating Centre for Methods and Tools, review 473.
Findings:
We screened 16 696 records at the title and abstract level, appraised 832 (5·0%) full texts, and initially included 73 (0·4%) studies. Of these, we excluded five (7%) studies because of critical risk of bias, leaving 68 (93%) studies that were extracted for analysis. For infections caused by any SARS-CoV-2 strain, vaccine effectiveness for the primary series reduced from 83% (95% CI 80-86) at baseline (14-42 days) to 62% (53-69) by 112-139 days. Vaccine effectiveness at baseline was 92% (88-94) for hospitalisations and 91% (85-95) for mortality, and reduced to 79% (65-87) at 224-251 days for hospitalisations and 86% (73-93) at 168-195 days for mortality. Estimated vaccine effectiveness was lower for the omicron variant for infections, hospitalisations, and mortality at baseline compared with that of other variants, but subsequent reductions occurred at a similar rate across variants. For booster doses, which covered mostly omicron studies, vaccine effectiveness at baseline was 70% (56-80) against infections and 89% (82-93) against hospitalisations, and reduced to 43% (14-62) against infections and 71% (51-83) against hospitalisations at 112 days or later. Not enough studies were available to report on booster vaccine effectiveness against mortality.
Interpretation:
Our analyses indicate that vaccine effectiveness generally decreases over time against SARS-CoV-2 infections, hospitalisations, and mortality. The baseline vaccine effectiveness levels for the omicron variant were notably lower than for other variants. Therefore, other preventive measures (eg, face-mask wearing and physical distancing) might be necessary to manage the pandemic in the long term.
Funding:
Canadian Institutes of Health Research and the Public Health Agency of Canada.
The largest burden of COVID-19 is carried by the elderly, and persons living in nursing homes are particularly vulnerable. However, 94% of the global population is younger than 70 years and 86% is younger than 60 years. The objective of this study was to accurately estimate the infection fatality rate (IFR) of COVID-19 among non-elderly people in the absence of vaccination or prior infection. In systematic searches in SeroTracker and PubMed (protocol: https://osf.io/xvupr), we identified 40 eligible national seroprevalence studies covering 38 countries with pre-vaccination seroprevalence data. For 29 countries (24 high-income, 5 others), publicly available age-stratified COVID-19 death data and age-stratified seroprevalence information were available and were included in the primary analysis. The IFRs had a median of 0.034% (interquartile range (IQR) 0.013–0.056%) for the 0–59 years old population, and 0.095% (IQR 0.036–0.119%) for the 0–69 years old. The median IFR was 0.0003% at 0–19 years, 0.002% at 20–29 years, 0.011% at 30–39 years, 0.035% at 40–49 years, 0.123% at 50–59 years, and 0.506% at 60–69 years. IFR increases approximately 4 times every 10 years. Including data from another 9 countries with imputed age distribution of COVID-19 deaths yielded median IFR of 0.025–0.032% for 0–59 years and 0.063–0.082% for 0–69 years. Meta-regression analyses also suggested global IFR of 0.03% and 0.07%, respectively in these age groups. The current analysis suggests a much lower pre-vaccination IFR in non-elderly populations than previously suggested. Large differences did exist between countries and may reflect differences in comorbidities and other factors. These estimates provide a baseline from which to fathom further IFR declines with the widespread use of vaccination, prior infections, and evolution of new variants.