What do currently active cases on Israeli MoH dashboard say about vaccine effectiveness vs. infection/severe disease for those vaccinated and with/without booster?
Here are the current data as of October 7, 2021 after roughly 2 months of delivery of boosters:
These data come from the Israel MoH dashboard from October 7, 2021 (datadashboard.health.gov.il/COVID-19/gener…) and include counts of currently active infections and severe infections by age group, split out by vaccination status (unvaccinated, boosters, or vaccinated but no boosters).
I simply computed "vaccine effectiveness" for vaccine (or booster) as the 1 minus the ratio of the infection (or serious infection) rate for the vaccinated (or boosted) group and the unvaccinated group.
Note that this is only a snapshot of current cases.
We see that for infection, VE is only 50-60% for those not boosted, while for the boosted group it is ~90-95%. Boosters clearly restore the protection vs. infection that has been shown to wane over time.
However, we still see un-boosted vaccinated still retain substantial protection at 55-60%.
In September, we know >1/3 of 20-59 unvax population was previously infected, so these VE may be attenuated by the subset of unvax with strong immune protection.
When we look at severe disease, we see even without boosters we see strong protection vs. severe disease in the vaccinated group that has not been boosted.
Of course the boosters top off that protection and push it near 100%
There is no question that boosters restore high levels of protection vs. infection and severe disease.
The question is whether they are necessary or desirable for all age groups, especially in the context of a long term strategy.
Is it reasonable to plan to give boosters every 4-6m into the future to maintain exceptionally high levels of neutralizing antibodies vs SARS-CoV-2 in our bloodstream?
Possibly, this makes sense during surges of the pandemic, but maybe not as a long term strategy.
Of course these simple VE estimates have limitations: 1. They are only based on a snapshot of "current" cases in Israel 2. While adjusting for age, they do not adjust for other confounders including co-morbidities, vaccination time, or sex/race and time infected.
3. As mentioned, previous infection does not appear to be separated out of MoH data, & these comprise a substantial proportion of unvaccinated. 4. There may be other factors determining who got boosted or not, and these may also bias the VE.
I still find these data informative.
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Lancet paper based on >3.4m USA patients found Pfizer vaccine effectiveness (VE) vs. infection decreased from 88% 1m after vaccination to 47% after 5m, but that VE vs. hospitalization remained strong at 93% through 6m.
This paper followed >3.4m patients >12yr old in the Kaiser Permanente Southern California (KPSC) system between 12/20 and 7/21 using a retrospective cohort design.
All patients needed to have >1yr of previous data to establish comorbidities.
Their primary analysis computed relative risk of PCR+ infection, comparing unvaccinated with fully vaccinated individuals for each calendar day.
They adjusted for age, sex, race, previous SARS-CoV-2 infection, SES, previous health-care utilization, & various co-morbidities.
Large contact tracing study in UK shows Pfizer vaccine reduces transmission by 82% vs alpha and 65% vs delta and AstraZeneca by 63% and 36%, respectively. medrxiv.org/content/10.110…
The study uses the national contacting tracing registry and compares testing positivity of contacts across vaccinated and unvaccinated, stratify information by vaccine type and number of doses
The modeling accounts for key potential confounding variables in the tested individual so as not to be driven by demographic factors.
When you are senior editor of a journal and handle your own paper, it is not peer review, it is an editorial:
I’ve now read the paper in detail
It is a science based commentary projecting authors’ viewpoints including 1. <<35k have actually died from covid 2. yet 225k-1.4m have already died from vaccines 3. With most of the paper describing why they think it is the tip of the iceberg
Their methodology for estimating vaccine caused death is hopelessly flawed, driven by an assumption that vaers death reporting is the same in the day or two after inoculation as it is months later.
This thread explores how time confouding can artificially inflate vaccine effectiveness (VE) estimates from observational data & make them misleading.
This may explain some reports earlier this year reporting 97-98%+ VE numbers, too high to be believable.
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The basic idea is: 1. Vaccination rates were very low in early 2021 2. COVID-19 infection/death rates were very high in early 2021 from winter surge
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3. Vaccination rates strongly increased moving from winter into spring/early summer 2021 4. COVID infection/death rates decreased moving from winter into spring/early summer coming off the winter surge and into the pre-Delta lull.
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Data presented below show nearly 33% of unvaccinated adult Israeli residents were previously infected.
Why is this important & has this contributed to misinterpretation of Israeli data?
This thread wll explore these questions.
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Israeli MoH releases periodic vaccination reports on its Telegram site. This table breaks down vaccination status by age groups as of September 14, 2021 listing total population and number given 1/2/3 doses plus those unvaccinated but recovered from previous infection 2/n
From these data, I constructed this table with % of population unvaccinated, given 1 dose, 2 doses, & 3 doses, & proportion of unvaccinated are previously infected.
Note that >30% of total unvaccinated Israelis were previously infected, & >1/3 for all age groups in 20-59yr 3/n
For older group, CFR for vaccinated (1.81%) is 3.3-fold LOWER than CFR for unvaccinated (5.96%)
For younger group, CFR for vaccinated (0.05%) is 1.5-fold higher than CFR for unvaccinated (0.03%), but there are only 13 deaths. 2/4
Another case of Simpson's paradox, since a confounding factor (age) is STRONGLY associated with both outcome (death) and exposure (vaccination status) given risk of death in old >>> young and vaccination rate old >>> young.
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