Did you hear about the Stanford study conducted by Catherine Axfors and John Ioannidis that showed extremely high recovery rates for anyone under the age of 70? No? Then you’re probably still getting your news from Google, Facebook, Twitter, or mainstream media. They have ignored or even censored the study.

Facebook went so far as to post a ludicrous “fact-check” written by an undergraduate with no scientific background. The article they use to “debunk” the study is older than the study itself.

Infection Fatality Rate (IFR) is arguably the most important statistic to use when making policy. At the very least it’s up there with R0 infectious rates statistics. In short, IFR is the percentage of people who have been infected by a disease who end up dying as a direct or indirect result. It excludes non-relevant deaths; someone whose body shows Covid-19 present after dying from a gunshot wound would not be included in proper IFR statistics (but don’t tell the CDC that because they’re not so sure).

The study shows the elderly are by far the most at-risk from Covid-19, but it also concludes that the statistics used by most governments are exaggerated. As for anyone under the age of 70, the IFR is very low, under 1% even for 69-year-olds.

Here is a breakdown of IFRs by age group with a distinction made between elderly living in communities and the elderly overall:

• Overall Elderly, 70+: 5.5%
• Elderly in Communities, 70+: 2.4%
• 60-69: 0.59%
• 50-59: 0.27%
• 40-49: 0.082%
• 30-39: 0.031%
• 20-29: 0.014%
• 0-19: 0.0027%

To put these numbers into perspective, out of 100,000 kids aged 0-19 who are infected by Covid-19, around three will die from it. For every 100 people over the age of 70 who are infected by Covid-19, around five will die from it. The difference in how Covid-19 affects different ages is exponential.

We are publishing the abstract, introduction, and methodology of the study. You can find the rest here if you enjoy reading such things. But the meat of the study can be found in the results that are quite eye-opening considering how unhinged the world has become over this disease. Below, I will add some commentary. Here is how the preprint study opens:

ABSTRACT

Background The infection fatality rate (IFR) of Coronavirus Disease 2019 (COVID-19) varies widely according to age and residence status.

Purpose Estimate the IFR of COVID-19 in community-dwelling elderly populations and other age groups from seroprevalence studies. Study protocol: https://osf.io/47cgb.

Data Sources Seroprevalence studies done in 2020 and identified by any of four existing systematic reviews.

Study Selection SARS-CoV-2 seroprevalence studies with ≥1000 participants aged ≥70 years that presented seroprevalence in elderly people; aimed to generate samples reflecting the general population; and whose location had available data on cumulative COVID-19 deaths in elderly (primary cutoff ≥70 years; ≥65 or ≥60 also eligible).

Data Extraction We extracted the most fully adjusted (if unavailable, unadjusted) seroprevalence estimates and sampling procedure details. We also extracted age- and residence-stratified cumulative COVID-19 deaths (until 1 week after the seroprevalence sampling midpoint) from official reports, and population statistics, to calculate IFRs corrected for unmeasured antibody types. Sample size-weighted IFRs were estimated for countries with multiple estimates. Secondary analyses examined data on younger age strata from the same studies.

Data Synthesis Twenty-three seroprevalence surveys representing 14 countries were included. Across all countries, the median IFR in community-dwelling elderly and elderly overall was 2.4% (range 0.3%-7.2%) and 5.5% (range 0.3%-12.1%). IFR was higher with larger proportions of people >85 years. Younger age strata had low IFR values (median 0.0027%, 0.014%, 0.031%, 0.082%, 0.27%, and 0.59%, at 0-19, 20-29, 30-39, 40-49, 50-59, and 60-69 years).

Limitations Biases in seroprevalence and mortality data.

Conclusions The IFR of COVID-19 in community-dwelling elderly people is lower than previously reported. Very low IFRs were confirmed in the youngest populations.

INTRODUCTION

Most Coronavirus Disease 2019 (COVID-19) affect the elderly (1), and persons living in nursing homes are particularly vulnerable (2). Hundreds of seroprevalence studies have been conducted in various populations, locations, and settings. These data have been used and synthesized in several published efforts to obtain estimates of the infection fatality rate (IFR, proportion of deceased among those infected), and its heterogeneity (3-6). All analyses identify very strong risk-gradient based on age, although absolute risk values still have substantial uncertainty. Importantly, the vast majority of seroprevalence studies include very few elderly people (7). Extrapolating from seroprevalence in younger to older age groups is tenuous. Elderly people may genuinely have different seroprevalence. Ideally, elderly should be more protected from exposure/infection than younger people, although probably the ability to protect the elderly has varied substantially across countries (8). Moreover, besides age, comorbidities and lower functional status markedly affects COVID-19 death risk (9). Particularly elderly nursing home residents accounted for 30-70% of COVID-19 deaths in high-income countries in the first wave (2), despite comprising <1% of the population. IFR in nursing home residents has been estimated to as high as 25% (10). Not separating residents of nursing homes from the community-dwelling may provide an average that is too low for the former and too high for the latter. Moreover, ascertainment and reporting of COVID-19 cases and deaths in nursing home populations show considerable variation across countries (2), with potentially heavy bearing on overall mortality, while community-dwelling elderly data may be less unreliable (especially in high-income countries). Finally, seroprevalence estimates reflect typically community-dwelling populations (enrollment of nursing home residents is scarce/absent in serosurveys).

Here we estimated the COVID-19 IFR in community-dwelling populations at all locations where seroprevalence studies with many elderly individuals have been conducted. Primary emphasis is on the IFR of the elderly. As a secondary analysis, we also explored the IFR of younger age-strata in these same studies.

METHODS

Data Sources and Searches

We identified seroprevalence studies (peer-reviewed publications, official reports, or preprints) in four existing systematic reviews (3, 7, 11, 12) as for a previous project (13), using the most recent updates of these reviews and their respective databases as of March 16, 2021. The protocol of this study was registered at the Open Science Framework (https://osf.io/47cgb) after piloting data availability in December 2020 but before extracting full data, communicating with local authorities and study authors for additional data and performing any calculations. Amendments to the protocol and their justification are described in Appendix Table 1.

Editor’s Commentary

There’s a reason Big Tech, mainstream media, and our own government are suppressing studies like this. It’s not unique, and invariably they come to the same basic conclusion. Covid-19 is risky for the elderly, not very risky for young- and middle-aged-adults, and nearly risk-free for people under the age of 20.

The reason this data is suppressed is because it doesn’t jibe the narrative that is being driven by an agenda. That agenda, Pandemic Panic Theater, is a control mechanism. This is how they force kids to wear face masks at school. It’s how they force lockdowns that clearly do not work. And it’s how they promote universal vaccination despite the fact that the vaccines do not appear to be working very well.

Any human with a brain that hasn’t been indoctrinated or dulled by fear should be asking questions about why the narrative is so adamantly being protected in spite of the science. Covid-19 is a serious disease, particularly for the elderly, but it does not warrant the reactions we’ve seen from governments, businesses, or the “arbiters of truth” in media, Big Tech, and academia.

Open your eyes, folks. We’re being lied to on a minute-by-minute basis. There is no other possible reason for such blatant censorship of any true science that betrays the established narrative.

Plandemic, Voter Fraud, Afghanistan: All Parts of The Great Reset

If you still consider “conspiracy theorist” to be a pejorative, you’re not paying enough attention.

What do Covid-19, 2020 (and beyond) election fraud, and our disastrous abandoning of American citizens in Afghanistan have to do with one another? They’re all parts of the globalist plan to usher in a new era of Neo-Marxism in a depopulated world. This is The Great Reset unfolding before our eyes, and the only way we can stop it (other than through constant prayer) is to stand together and spread the truth.

Covid-19’s attachment to The Great Reset is obvious, though we continue to post about it daily. Voter fraud is a bit less obvious but nonetheless easy to recognize when we understand that people like Joe Biden, Justin Trudeau, and Emmanuel Macron are pushing to “Build Back Better” under the precepts of Neo-Marxism.

The Afghanistan betrayal in August, 2021, is harder to associate with The Great Reset, but it’s definitely just as attached. We can see this is the unwillingness of the Biden regime to extend the deadline to withdraw despite American lives put clearly in harm’s way. We can see it in the needlessly abandoned military equipment that was conspicuously left intact; disabling a Black Hawk takes minutes but they were given to the Taliban in pristine working condition. Then, there’s the pallets of hundred dollar bills left for them. If you think all of this was just irresponsible governance, you probably think Barack Obama is out of politics, too.

Weakening America’s standing in the world and empowering the Chinese Communist Party and others to engage fully with our enemies in a globalist cabal are beneficial to the architects of The Great Reset, and both happened in one fell swoop in August.

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