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All-cause Mortality During Covid-19 Vaccinations in European Active Populations
Abstract
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
ResearchGate has not been able to resolve any citations for this publication.
Seventeen equatorial and Southern-Hemisphere countries were studied (Argentina, Australia, Bolivia, Brazil, Chile, Colombia, Ecuador, Malaysia, New Zealand, Paraguay, Peru, Philippines, Singapore, South Africa, Suriname, Thailand, Uruguay), which comprise 9.10 % of worldwide population, 10.3 % of worldwide COVID-19 injections (vaccination rate of 1.91 injections per person, all ages), virtually every COVID-19 vaccine type and manufacturer, and span 4 continents.
In the 17 countries, there is no evidence in all-cause mortality (ACM) by time data of any beneficial effect of COVID-19 vaccines. There is no association in time between COVID-19 vaccination and any proportionate reduction in ACM. The opposite occurs.
All 17 countries have transitions to regimes of high ACM, which occur when the COVID-19 vaccines are deployed and administered. Nine of the 17 countries have no detectable excess ACM in the period of approximately one year after a pandemic was declared on 11 March 2020 by the World Health Organization (WHO), until the vaccines are rolled out (Australia, Malaysia, New Zealand, Paraguay, Philippines, Singapore, Suriname, Thailand, Uruguay).
Unprecedented peaks in ACM occur in the summer (January-February) of 2022 in the Southern Hemisphere, and in equatorial-latitude countries, which are synchronous with or immediately preceded by rapid COVID-19-vaccine-booster-dose rollouts (3rd or 4th doses). This phenomenon is present in every case with sufficient mortality data (15 countries). Two of the countries studied have insufficient mortality data in January- February 2022 (Argentina and Suriname).
Detailed mortality and vaccination data for Chile and Peru allow resolution by age and by dose number. It is unlikely that the observed peaks in all-cause mortality in January- February 2022 (and additionally in: July-August 2021, Chile; July-August 2022, Peru), in each of both countries and in each elderly age group, could be due to any cause other than the temporally associated rapid COVID-19-vaccine-booster-dose rollouts. Likewise, it is unlikely that the transitions to regimes of high ACM, coincident with the rollout and sustained administration of COVID-19 vaccines, in all 17 Southern- Hemisphere and equatorial-latitude countries, could be due to any cause other than the vaccines.
Synchronicity between the many peaks in ACM (in 17 countries, on 4 continents, in all elderly age groups, at different times) and associated rapid booster rollouts allows this firm conclusion regarding causality, and accurate quantification of COVID-19-vaccine toxicity.
The all-ages vaccine-dose fatality rate (vDFR), which is the ratio of inferred vaccine- induced deaths to vaccine doses delivered in a population, is quantified for the January- February 2022 ACM peak to fall in the range 0.02 % (New Zealand) to 0.20 % (Uruguay). In Chile and Peru, the vDFR increases exponentially with age (doubling approximately every 4 years of age), and is largest for the latest booster doses, reaching approximately 5 % in the 90+ years age groups (1 death per 20 injections of dose 4). Comparable results occur for the Northern Hemisphere, as found in previous articles (India, Israel, USA).
We quantify the overall all-ages vDFR for the 17 countries to be (0.126 ± 0.004) %, which would imply 17.0 ± 0.5 million COVID-19 vaccine deaths worldwide, from 13.50 billion injections up to 2 September 2023. This would correspond to a mass iatrogenic event that killed (0.213 ± 0.006) % of the world population (1 death per 470 living persons, in less than 3 years), and did not measurably prevent any deaths.
The overall risk of death induced by injection with the COVID-19 vaccines in actual populations, inferred from excess all-cause mortality and its synchronicity with rollouts, is globally pervasive and much larger than reported in clinical trials, adverse effect monitoring, and cause-of-death statistics from death certificates, by 3 orders of magnitude (1,000-fold greater).
The large age dependence and large values of vDFR quantified in this study of 17 countries on 4 continents, using all the main COVID-19 vaccine types and manufacturers, should induce governments to immediately end the baseless public health policy of prioritizing elderly residents for injection with COVID-19 vaccines, until valid risk-benefit analyses are made.
The COVID-19 pandemic caused much illness, many deaths, and profound disruption to society. The production of ‘safe and effective’ vaccines was a key public health target. Sadly, unprecedented high rates of adverse events have overshadowed the benefits. This two-part narrative review presents evidence for the widespread harms of novel product COVID-19 mRNA and adenovectorDNA vaccines and is novel in attempting to provide a thorough overview of harms arising from the new technology in vaccines that relied on human cells producing a foreign antigen that has evidence of pathogenicity. This first paper explores peer-reviewed data counter to the ‘safe and effective’ narrative attached to these new technologies. Spike protein pathogenicity, termed ‘spikeopathy’, whether from the SARS-CoV-2 virus or produced by vaccine gene codes, akin to a ‘synthetic virus’, is increasingly understood in terms of molecular biology and pathophysiology. Pharmacokinetic transfection through body tissues distant from the injection site by lipid-nanoparticles or viral-vector carriers means that ‘spikeopathy’ can affect many organs. The inflammatory properties of the nanoparticles used to ferry mRNA; N1-methylpseudouridine employed to prolong synthetic mRNA function; the widespread biodistribution of the mRNA and DNA codes and translated spike proteins, and autoimmunity via human production of foreign proteins, contribute to harmful effects. This paper reviews autoimmune, cardiovascular, neurological, potential oncological effects, and autopsy evidence for spikeopathy. With many gene-based therapeutic technologies planned, a re-evaluation is necessary and timely.
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.
Several studies have reported associations between COVID-19 vaccination and risk of cardiac diseases, especially in young people; the impact on mortality, however, remains unclear. We use national, linked electronic health data in England to assess the impact of COVID-19 vaccination and positive SARS-CoV-2 tests on the risk of cardiac and all-cause mortality in young people (12 to 29 years) using a self-controlled case series design. Here, we show there is no significant increase in cardiac or all-cause mortality in the 12 weeks following COVID-19 vaccination compared to more than 12 weeks after any dose. However, we find an increase in cardiac death in women after a first dose of non mRNA vaccines. A positive SARS-CoV-2 test is associated with increased cardiac and all-cause mortality among people vaccinated or unvaccinated at time of testing.
The COVID-19 pandemic triggered an unprecedented rise in mortality that translated into life expectancy losses around the world, with only a few exceptions. We estimate life expectancy changes in 29 countries since 2020 (including most of Europe, the United States and Chile), attribute them to mortality changes by age group and compare them with historic life expectancy shocks. Our results show divergence in mortality impacts of the pandemic in 2021. While countries in western Europe experienced bounce backs from life expectancy losses of 2020, eastern Europe and the United States witnessed sustained and substantial life expectancy deficits. Life expectancy deficits during fall/winter 2021 among people ages 60+ and <60 were negatively correlated with measures of vaccination uptake across countries (r60+ = −0.86; two-tailed P < 0.001; 95% confidence interval, −0.94 to −0.69; r<60 = −0.74; two-tailed P < 0.001; 95% confidence interval, −0.88 to −0.46). In contrast to 2020, the age profile of excess mortality in 2021 was younger, with those in under-80 age groups contributing more to life expectancy losses. However, even in 2021, registered COVID-19 deaths continued to account for most life expectancy losses.
This study focuses on cardiovascular manifestation, particularly myocarditis and pericarditis events, after BNT162b2 mRNA COVID-19 vaccine injection in Thai adolescents. This prospective cohort study enrolled students aged 13–18 years from two schools, who received the second dose of the BNT162b2 mRNA COVID-19 vaccine. Data including demographics, symptoms, vital signs, ECG, echocardiography, and cardiac enzymes were collected at baseline, Day 3, Day 7, and Day 14 (optional) using case record forms. We enrolled 314 participants; of these, 13 participants were lost to follow-up, leaving 301 participants for analysis. The most common cardiovascular signs and symptoms were tachycardia (7.64%), shortness of breath (6.64%), palpitation (4.32%), chest pain (4.32%), and hypertension (3.99%). One participant could have more than one sign and/or symptom. Seven participants (2.33%) exhibited at least one elevated cardiac biomarker or positive lab assessments. Cardiovascular manifestations were found in 29.24% of patients, ranging from tachycardia or palpitation to myopericarditis. Myopericarditis was confirmed in one patient after vaccination. Two patients had suspected pericarditis and four patients had suspected subclinical myocarditis. In conclusion, Cardiovascular manifestation in adolescents after BNT162b2 mRNA COVID-19 vaccination included tachycardia, palpitation, and myopericarditis. The clinical presentation of myopericarditis after vaccination was usually mild and temporary, with all cases fully recovering within 14 days. Hence, adolescents receiving mRNA vaccines should be monitored for cardiovascular side effects. Clinical Trial Registration: NCT05288231.
The present study estimates the burden of COVID-19 on mortality. The state-of-the-art method of actuarial science is used to estimate the expected number of all-cause deaths
in 2020 to 2022, if there had been no pandemic. Then the number of observed all-cause
deaths is compared with this expected number of all-cause deaths, yielding the excess
mortality in Germany for the pandemic years 2020 to 2022.
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. Municipal demographics such as age, culture and population density are strong confounders of mortality and vaccine-uptake. We account for these by normalizing results to prepandemic year 2019, where covid was absent but demographics were highly representative for later years. Normalized to 2019, we found no correlation between municipal mortality in 2020 with vaccination uptake in 2021, which shows the effectiveness of our confounder accounting. We could not observe a mortality-reducing effect of vaccination in Dutch municipalities after vaccination and booster campaigns. We did find a 4-sigma-significant mortality-enhancing effect during the two periods of high unexplained excess mortality. Our results add to other recent findings of zero mRna-vaccine effectiveness on all-cause mortality, calling for more research on this topic.
Background:
The first COVID-19 vaccine outside a clinical trial setting was administered on Dec 8, 2020. To ensure global vaccine equity, vaccine targets were set by the COVID-19 Vaccines Global Access (COVAX) Facility and WHO. However, due to vaccine shortfalls, these targets were not achieved by the end of 2021. We aimed to quantify the global impact of the first year of COVID-19 vaccination programmes.
Methods:
A mathematical model of COVID-19 transmission and vaccination was separately fit to reported COVID-19 mortality and all-cause excess mortality in 185 countries and territories. The impact of COVID-19 vaccination programmes was determined by estimating the additional lives lost if no vaccines had been distributed. We also estimated the additional deaths that would have been averted had the vaccination coverage targets of 20% set by COVAX and 40% set by WHO been achieved by the end of 2021.
Findings:
Based on official reported COVID-19 deaths, we estimated that vaccinations prevented 14·4 million (95% credible interval [Crl] 13·7-15·9) deaths from COVID-19 in 185 countries and territories between Dec 8, 2020, and Dec 8, 2021. This estimate rose to 19·8 million (95% Crl 19·1-20·4) deaths from COVID-19 averted when we used excess deaths as an estimate of the true extent of the pandemic, representing a global reduction of 63% in total deaths (19·8 million of 31·4 million) during the first year of COVID-19 vaccination. In COVAX Advance Market Commitment countries, we estimated that 41% of excess mortality (7·4 million [95% Crl 6·8-7·7] of 17·9 million deaths) was averted. In low-income countries, we estimated that an additional 45% (95% CrI 42-49) of deaths could have been averted had the 20% vaccination coverage target set by COVAX been met by each country, and that an additional 111% (105-118) of deaths could have been averted had the 40% target set by WHO been met by each country by the end of 2021.
Interpretation:
COVID-19 vaccination has substantially altered the course of the pandemic, saving tens of millions of lives globally. However, inadequate access to vaccines in low-income countries has limited the impact in these settings, reinforcing the need for global vaccine equity and coverage.
Funding:
Schmidt Science Fellowship in partnership with the Rhodes Trust; WHO; UK Medical Research Council; Gavi, the Vaccine Alliance; Bill & Melinda Gates Foundation; National Institute for Health Research; and Community Jameel.
Myocarditis and pericarditis are potential post-acute cardiac sequelae of COVID-19 infection, arising from adaptive immune responses. We aimed to study the incidence of post-acute COVID-19 myocarditis and pericarditis. Retrospective cohort study of 196,992 adults after COVID-19 infection in Clalit Health Services members in Israel between March 2020 and January 2021. Inpatient myocarditis and pericarditis diagnoses were retrieved from day 10 after positive PCR. Follow-up was censored on 28 February 2021, with minimum observation of 18 days. The control cohort of 590,976 adults with at least one negative PCR and no positive PCR were age- and sex-matched. Since the Israeli vaccination program was initiated on 20 December 2020, the time-period matching of the control cohort was calculated backward from 15 December 2020. Nine post-COVID-19 patients developed myocarditis (0.0046%), and eleven patients were diagnosed with pericarditis (0.0056%). In the control cohort, 27 patients had myocarditis (0.0046%) and 52 had pericarditis (0.0088%). Age (adjusted hazard ratio [aHR] 0.96, 95% confidence interval [CI]; 0.93 to 1.00) and male sex (aHR 4.42; 95% CI, 1.64 to 11.96) were associated with myocarditis. Male sex (aHR 1.93; 95% CI 1.09 to 3.41) and peripheral vascular disease (aHR 4.20; 95% CI 1.50 to 11.72) were associated with pericarditis. Post COVID-19 infection was not associated with either myocarditis (aHR 1.08; 95% CI 0.45 to 2.56) or pericarditis (aHR 0.53; 95% CI 0.25 to 1.13). We did not observe an increased incidence of neither pericarditis nor myocarditis in adult patients recovering from COVID-19 infection.
The question whether COVID-19 vaccines have no effect on all-cause mortality or perform as intended, that is mainly reduce excess mortality, has been debated recently in the scientific literature. By crossing the all-cause mortality data with the vaccine data from public European databases, we compare the impact on mortality of two variables of interest namely a vaccine-dose-rate and a covid-case-rate. Using classical machine learning strategies and graphical models, we are able to assess the conflicting hypothesis about the effect of vaccines on all-cause mortality, at least in Europe. Our conclusions differ for different age-categories investigated but, until a better predictive variable is found, our results clearly suggest that the benefit-risk balance for the 0-44 years old is not in favor of those vaccines.
This paper has been updated and the new version can be found here:
Official mortality data for England suggest systematic miscategorisation of vaccine status and uncertain effectiveness of Covid-19 vaccination
UPDATED WITH ONS DECEMBER DATA RELEASE & HEALTHY VACCINEE/MORIBUND ANALYSIS
http://dx.doi.org/10.13140/RG.2.2.28055.09124
https://www.researchgate.net/publication/357778435_Official_mortality_data_for_England_suggest_systematic_miscategorisation_of_vaccine_status_and_uncertain_effectiveness_of_Covid-19_vaccination
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The risk/benefit of Covid vaccines is arguably most accurately measured by an all-cause mortality rate comparison 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 the latest UK ONS vaccine mortality surveillance report which provides the necessary information to monitor this crucial comparison over time. At first glance the ONS data suggest that, in each of the older age groups, all-cause mortality is lower in the vaccinated than the unvaccinated. Despite this apparent evidence to support vaccine effectiveness-at least for the older age groups-on closer inspection of this data, this conclusion is cast into doubt because of a range of fundamental inconsistencies and anomalies in the data. Whatever the explanations for the observed data, it is clear that it is both unreliable and misleading. While socio-demographical and behavioural differences between vaccinated and unvaccinated have been proposed as possible explanations, there is no evidence to support any of these. By Occam's razor we believe the most likely explanations are systemic miscategorisation of deaths between the different categories of unvaccinated and vaccinated; delayed or non-reporting of vaccinations; systemic underestimation of the proportion of unvaccinated; and/or incorrect population selection for Covid deaths.
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Policy makers and mainstream news anchors have promised the public that the COVID-19 vaccine rollout worldwide would reduce symptoms, and thereby cases and deaths associated with COVID-19. While this vaccine rollout is still in progress, there is a large amount of public data available that permits an analysis of the effect of the vaccine rollout on COVID-19 related cases and deaths. Has this public policy treatment produced the desired effect?
One manner to respond to this question can begin by implementing a Bayesian causal analysis comparing both pre- and post-treatment periods. This study analyzed publicly available COVID-19 data from OWID utlizing the R package CausalImpact to determine the causal effect of the administration of vaccines on two dependent variables that have been measured cumulatively throughout the pandemic: total deaths per million (y1) and total cases per million (y2). After eliminating all results from countries with p > 0.05, there were 128 countries for y1 and 103 countries for y2 to analyze in this fashion, comprising 145 unique countries in total (avg. p < 0.004).
Results indicate that the treatment (vaccine administration) has a strong and statistically significant propensity to causally increase the values in either y1 or y2 over and above what would have been expected with no treatment. y1 showed an increase/decrease ratio of (+115/-13), which means 89.84% of statistically significant countries showed an increase in total deaths per million associated with COVID-19 due directly to the causal impact of treatment initiation. y2 showed an increase/decrease ratio of (+105/-16) which means 86.78% of statistically significant countries showed an increase in total cases per million of COVID-19 due directly to the causal impact of treatment initiation. Causal impacts of the treatment on y1 ranges from -19% to +19015% with an average causal impact of +463.13%. Causal impacts of the treatment on y2 ranges from -46% to +12240% with an average causal impact of +260.88%. Hypothesis 1 Null can be rejected for a large majority of countries.
This study subsequently performed correlational analyses on the causal impact results, whose effect variables can be represented as y1.E and y2.E respectively, with the independent numeric variables of: days elapsed since vaccine rollout began (n1), total vaccination doses per hundred (n2), total vaccine brands/types in use (n3) and the independent categorical variables continent (c1), country (c2), vaccine variety (c3). All categorical variables showed statistically significant (avg. p: < 0.001) postive Wilcoxon signed rank values (y1.E V:[c1 3.04; c2: 8.35; c3: 7.22] and y2.E V:[c1 3.04; c2: 8.33; c3: 7.19]). This demonstrates that the distribution of y1.E and y2.E was non-uniform among categories. The Spearman correlation between n2 and y2.E was the only numerical variable that showed statistically significant results (y2.E ~ n2: rho: 0.34 CI95%[0.14, 0.51], p: 4.91e-04). This low positive correlation signifies that countries with higher vaccination rates do not have lower values for y2.E, slightly the opposite in fact. Still, the specifics of the reasons behind these differences between countries, continents, and vaccine types is inconclusive and should be studied further as more data become available. Hypothesis 2 Null can be rejected for c1, c2, c3 and n2 and cannot be rejected for n1, and n3.
The statistically significant and overwhelmingly positive causal impact after vaccine deployment on the dependent variables total deaths and total cases per million should be highly worrisome for policy makers. They indicate a marked increase in both COVID-19 related cases and death due directly to a vaccine deployment that was originally sold to the public as the “key to gain back our freedoms.” The effect of vaccines on total cases per million and its low positive association with total vaccinations per hundred signifies a limited impact of vaccines on lowering COVID-19 associated cases. These results should encourage local policy makers to make policy decisions based on data, not narrative, and based on local conditions, not global or national mandates. These results should also encourage policy makers to begin looking for other avenues out of the pandemic aside from mass vaccination campaigns.
Some variables that could be included in future analyses might include vaccine lot by country, the degree of prevalence of previous antibodies against SARS-CoV or SARS-CoV-2 in the population before vaccine administration begins, and the Causal Impact of ivermectin on the same variables used in this study.
Importance
Although there are reports of COVID-19 vaccine implementation in real-world populations, these come from high-income countries or from experience with messenger RNA technology vaccines. Data on outcomes of vaccine deployment in low- or middle-income countries are lacking.
Objective
To assess whether the pragmatic application of the 3 COVID-19 vaccines available in Argentina, 2 of which have no reports of evaluation in real-world settings to date, were associated with a reduction in morbidity, all-cause mortality, and mortality due to COVID-19.
Design, Setting, and Participants
This cohort study used individual and ecological data to explore outcomes following vaccination with rAd26-rAd5, ChAdOx1, and BBIBP-CorV. To correct for differences in exposure times, results are shown using incidence density per 100 000 person-days from the start of the vaccination campaign (December 29, 2020) to the occurrence of an event or the end of follow-up (May 15, 2021). Participants included 663 602 people aged at least 60 years residing in the city of Buenos Aires, Argentina. Statistical analysis was performed from June 1 to June 15, 2021.
Main Outcomes and Measures
Diagnosis of COVID-19 confirmed by reverse transcription–polymerase chain reaction, death from all causes, and death within 30 days of a diagnosis of COVID-19. Poisson regression models were fitted to estimate associations with all 3 outcomes.
Results
Among 663 602 residents of the city of Buenos Aires included in the study, 540 792 (81.4%) were vaccinated with at least 1 dose, with 457 066 receiving 1 dose (mean [SD] age, 74.5 (8.9) years; 61.5% were female [n = 281 284]; 68.0% [n = 310 987] received the rAd26-rAd5 vaccine; 29.5% [n = 135 036] received ChAdOx1; 2.4% [n = 11 043] received BBIBP-CorV) and 83 726 receiving 2 doses (mean [SD] age, 73.4 [6.8] years; 63.5% were female [n = 53 204]). The incidence density of confirmed COVID-19 was 36.25 cases/100 000 person-days (95% CI, 35.80-36.70 cases/100 000 person-days) among those who did not receive a vaccine, 19.13 cases/100 000 person-days (95% CI, 18.63-19.62 cases/100 000 person-days) among those who received 1 dose, and 4.33 cases/100 000 person-days (95% CI, 3.85-4.81 cases/100 000 person-days) among those who received 2 doses. All-cause mortality was 11.74 cases/100 000 person-days (95% CI, 11.51-11.96 cases/100 000 person-days), 4.01 cases/100 000 person-days (95% CI, 3.78-4.24 cases/100 000 person-days) and 0.40 cases/100 000 person-days (95% CI, 0.26-0.55 cases/100 000 person-days). COVID-19–related-death rate was 2.31 cases/100 000 person-days (95% CI, 2.19-2.42 cases/100 000 person-days), 0.59 cases/100 000 person-days (95% CI, 0.50-0.67 cases/100 000 person-days), and 0.04 cases/100 000 person-days (95% CI, 0.0-0.09 cases/100 000 person-days) among the same groups. A 2-dose vaccination schedule was associated with an 88.1% (95% CI, 86.8%-89.2%) reduction in documented infection, 96.6% (95% CI, 95.3%-97.5%) reduction in all-cause death, and 98.3% (95% CI, 95.3%-99.4%) reduction in COVID-19–related death. A single dose was associated with a 47.2% (95% CI, 44.2%-50.1%) reduction in documented infection, 65.8% (95% CI, 61.7%-69.5%) reduction in all-cause death, and 74.5% (95% CI, 66%-80.8%) reduction in COVID-19–related death.
Conclusions and Relevance
This study found that within the first 5 months after the start of the vaccination campaign, vaccination was associated with a significant reduction in COVID-19 infection as well as a reduction in mortality.
Given the limitations of the randomized controlled trials (RCTs) for Covid19 vaccines, we must increasingly rely on data from observational studies to determine vaccine effectiveness. But over-simplistic reporting of such data can lead to obviously flawed conclusions due to statistical paradoxes. For example, if we just compare the total number of Covid19 deaths among the vaccinated and unvaccinated then we are likely to reach a different conclusion about vaccine effectiveness than if we make the same comparison in each age category. But age is just one of many factors that can confound the overall results in observational studies. Differences in the way we classify whether a person is vaccinated or is a Covid19 case can also result in very different conclusions. There are many critical interacting causal factors that can impact the overall results presented in studies of vaccine effectiveness. Causal models and Bayesian inference can in principle be used to both explain observed data and simulate the effect of controlling for confounding variables. However, this still requires data about relevant factors and much of these data are missing from the observational studies (and the RCTs). Hence their results may be unreliable. In the absence of such data, we believe the simplest and most conclusive evidence of vaccine evidence is to compare all-cause deaths for each age category between those who were unvaccinated and those who had previously had at least one vaccine dose.
In this paper, we analyze excess mortality in Israel during the COVID-19 crisis, focusing on the age group of young adults under 50 years of age, as their susceptibility to COVID-19 mortality is low. Based primarily on online data from the Central Bureau of Statistics [https://www.cbs.gov.il/he/publications/LochutTlushim/2020/p-1.xlsx] of Israel, we observed an unexpected rise of excess mortality among 20 to 49-year-olds in February-March 2021. It should be noted that excess mortality peaks among these young age groups are rarely observed, with low number of deaths that are usually caused by wars. We examined whether COVID-19 could account for this excess mortality. The inconsistency between the reported COVID-19 deaths and the excess deaths within this age group led to consider other potential causes: accident and vaccination. Indeed, the surge in mortality coincided with the rollout of the Israeli vaccination campaign for the 20 sto 49-year-olds, which reached more than 75% of individuals in this age group. This unexpected rise in excess mortality among young adults was also found in two other countries, the United Kingdom and Hungary, which have in common with Israel a massive vaccination of their populations. Thus, our observations should prompt to pause the campaign, while clarifying the underlying reasons for those excess deaths, especially in the context of a low mortality risk from COVID-19 within adults under 50 years of age.
Annual suicide figures are critical in identifying trends and guiding research, yet challenges arising from significant lags in reporting can delay and complicate real-time interventions. In this paper, we utilized Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland between 2004 and 2015. Official suicide rates are recorded by the Central Statistics Office in Ireland. While similar investigations using Google trends data have been carried out in other jurisdictions (e.g., United Kingdom, United Stated of America), such research had not yet been completed in Ireland. We compiled a collection of suicide- and depression-related search terms suggested by Google Trends and manually sourced from the literature. Monthly search rate terms at different lags were compared with suicide occurrences to determine the degree of correlation. Following two approaches based on vector autoregression and neural network autoregression, we achieved mean absolute error values between 4.14 and 9.61 when incorporating search query data, with the highest performance for the neural network approach. The application of this process to United Kingdom suicide and search query data showed similar results, supporting the benefit of Google Trends, neural network approach, and the applied search terms to forecast suicide risk increase. Overall, the combination of societal data and online behavior provide a good indication of societal risks; building on past research, our improvements led to robust models integrating search query and unemployment data for suicide risk forecasting in Ireland.
We sought to develop a practical influenza forecast model, based on real-time, geographically focused, and easy to access data, to provide individual medical centers with advanced warning of the number of influenza cases, thus allowing sufficient time to implement an intervention. Secondly, we evaluated how the addition of a real-time influenza surveillance system, Google Flu Trends, would impact the forecasting capabilities of this model. The final model selection demonstrated that autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance.
Objective
To compare the time trends of Google search interest in methamphetamine and criminal offences related to this drug.
Methods
Google Trends data for the search term "meth" was compared to methamphetamine-related crime statistics (incl. use, possession, and dealing) in Switzerland, Germany, and Austria for the years 2004–2016. Google data was availably monthly. Crime data was available yearly, and monthly values were imputed.
Results
On the country level, internet search trends for "meth" roughly paralleled relevant criminal activity. State-level data, which was available for Austria, showed more heterogeneity. Cross-correlations for yearly data almost always peaked at a lag time of 0 and coefficients were mostly between 0.7 and 1.0 on the country level, and between 0.5 to 1.0 on the state level. Monthly cross-correlations based on imputed values were substantially lower, ranging from 0 to 0.6.
Conclusions
These results encourage the further evaluation by law enforcement authorities of Google search activity as a possible predictor of methamphetamine-related crime. However, several limitations, in particular the crude temporal resolution of available crime data, precluded a detailed assessment of the relationship between internet search trends and the development of methamphetamine-related crime in central Europe.
This paper presents a novel method for the reconstruction of a neural network
connectivity using calcium fluorescence data. We introduce a fast unsupervised
method to integrate different networks that reconstructs structural
connectivity from neuron activity. Our method improves the state-of-the-art
reconstruction method General Transfer Entropy (GTE). We are able to better
eliminate indirect links, improving therefore the quality of the network via a
normalization and ensemble process of GTE and three new informative features.
The approach is based on a simple combination of networks, which is remarkably
fast. The performance of our approach is benchmarked on simulated time series
provided at the connectomics challenge and also submitted at the public
competition.
The importance of bringing causality into play when designing feature selection meth- ods is more and more acknowledged in the machine learning community. This paper proposes a filter approach based on infor- mation theory which aims to prioritise di- rect causal relationships in feature selection problems where the ratio between the num- ber of features and the number of samples is high. This approach is based on the no- tion of interaction which is shown to be informative about the relevance of an in- put subset as well as its causal relationship with the target. The resulting filter, called mIMR (min-Interaction Max-Relevance), is compared with state-of-the-art approaches. Classification results on 25 real microar- ray datasets show that the incorporation of causal aspects in the feature assessment is beneficial both for the resulting accuracy and stability. A toy example of causal discovery shows the effectiveness of the filter for iden- tifying direct causal relationships.
This study compares the COVID-19 per capita overall and excess mortality rates in the US vs rates for 20 Organization for Economic Co-operation and Development countries and the timing of any increases in excess mortality between June 2021 and December 2021 (Delta) and December 2021 to March 2022 (Omicron).
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.
This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity. Our method improves the state-of-the-art reconstruction method General Transfer Entropy (GTE). We are able to better eliminate indirect links, improving therefore the quality of the network via a normalization and ensemble process of GTE and three new informative features. The approach is based on a simple combination of networks, which is remarkably fast. The performance of our approach is benchmarked on simulated time series provided at the connectomics challenge and also submitted at the public competition.
Covid-19 vaccines and treatments: we must have raw data, now
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Uk cause of death project -death and disability trends, ages 15-44: Cadiovascular diseases
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Increased emergency cardiovascular events among under-40 population in israel during vaccine rollout and third covid-19 wave
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Australian covid-19 pandemic: A bradford hill analysis of iatrogenic excess mortality
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C., Goareguer, F., Longneaux, J.-M., Meyer, P. E., Mucchielli, L., Paul, E., Rentier,
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Covid vaccination and age-stratied all-cause mortality risk
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Life expectancy changes since covid-19
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Dowd, J. B., and Kashyap, R. (2022). Life expectancy changes since covid-19. Nature
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Increased emergency cardiovascular events among under-40 population in israel during vaccine rollout and third covid-19 wave
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Sun, C. L. F., Jae, E., and Levi, R. (2022). Increased emergency cardiovascular
events among under-40 population in israel during vaccine rollout and third covid-19
wave. Nature Reports.
Risk of death following covid-19 vaccination or positive sars-cov-2 test in young people in england
- V Nafilyan
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