US Disability Data
Part 7 - Relationship with hospitalisations following vaccination reported in V-Safe
Data Sources:
Update Date: March - 2023
Overview:
Using a completely independent database, namely the V-Safe database (described here), we estimate time series of hospitalisation events, conditional upon different thresholds for days lost at work, to obtain time series that represent different levels of severity of hospitalisation events. We remind the reader that in Part 3 of the V-Safe data analysis, a hospitalisation event was defined as one that led to emergency care or hospitalisation. Consequently, some hospitalisation events might not have been associated with an overnight stay, but simply as an emergency consultation and observation period.
Our analysis shows that during the vaccine rollout process, the CDC had live real world data that corroborated the safety signals observed in the clinical trials This data could and should have been used to anticipate the harm of the vaccine rollout on a population level.
Prior work:
In part 5 of our US disabilities analysis we observed that the rise in disability rates after Jan-2021 not only correlates closely with the rollout of the vaccination schedule, but also with the rate of Serious Adverse Events (SAEs) in the Moderna and Pfizer clinical trials. These rates are also of similar order of magnitude and therefore can explain the rise in disabilities.
Additionally we showed in Part 6 of our US disabilities analysis that by modelling the rate of Severe Adverse Events (Sev AEs), using data obtained from the Pfizer clinical trial, we could explain the pool of individuals that we believe were the source of the rise in disabilities from early 2021.
Estimating a Time Series of Hospitalisations from the V-Safe Data
Our purpose is to investigate the relationship between the rise in disabilities observed in the BLS data with an estimate of hospitalisation events that would occur through the mass Covid-19 vaccination rollout. Even though there is not necessarily a direct link between a hospitalisation event and a future disability, it is reasonable to consider that most disabled individuals might have passed through the process of 1) experiencing an adverse reaction and 2) seeking emergency care with consequent hospitalisation and missed work.
Likewise, it is reasonable to consider the link between the projected time series of hospitalisation events and with the modelled time-series of Serious Adverse Events (SAEs) or Severe Adverse Events (Sev AEs) that were discussed in Part 5 and Part 6 of our US disabilities analysis.
The V-Safe database comprises a large sample of individuals that could be used to infer hospitalisation events on the population level. However, it should be used with caution as the V-Safe app was likely used by tech-savvy individuals, who, consequently, might be a younger, more pro-vaccine cohort which may not accurately represent the overall population.
Assumptions for computing the time series of hospitalisation events
To obtain a time series of hospitalisation events during the vaccine rollout, we compute the rate of hospitalisations following dose 2 of inoculation. Because we do not count hospitalisation events occurring before dose 2, the rate of hospitalisation is lower than the actual rate of hospitalisation events.
In order to obtain time series of different severities of events, we add a condition for individuals who missed work for 2 or more days (milder outcomes), or 5 or more days (which is the more severe outcomes).
We then multiply the rate of hospitalisation events following dose 2 by the population, aged 19-64, who received a Covid-19 vaccine (any dose or brand) each month, based on CDC vaccination numbers. This number is then halved to account for the majority of people receiving 2 doses. To simplify the analysis, we assume that the hospitalisation events occur on the date of vaccination, which we know is likely to be incorrect as the rate of SAEs declines monotonically over time following vaccination, (shown in part 3 ) of the V-Safe data analysis. Furthermore, assuming that half of the monthly doses are dose 2 is slightly inaccurate, especially in the early months of rollout.
To summarise:
-> Each time series of the rate of hospitalisations is calculated as the number of hospitalisation events following dose 2 (and that led to a 2+ or 5+ number of days of lost work), divided by the number of dose 2 inoculations.
-> The measured hospitalisation rate with 2+ days of lost work was 4.18% and the hospitalisation rate with 5+ number of days of lost work was 1.67%
-> Hospitalisation events are expected to occur at a certain baseline level, independently of individuals taking the vaccine or not, so an adjustment factor was applied to estimate the rate of hospitalisations associated with dose 2 inoculation versus the background hospitalisation placebo rate.
-> The adjustment factor was taken from the rate, over the placebo rate, of Severe Adverse Events (Sev AEs) for the Pfizer trial. These were published in table S3 of the supplementary appendix of the paper by S. J. Thomas et al. that established the vaccine efficacy ("Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine through 6 Months", in New England Journal of Medicine, 9/2021). S.J. Thomas et al. estimated a risk ratio of 1.74 of Severe Adverse reactions of those individuals who were inoculated with the Pfizer vaccine relative to the placebo group.
-> We assume that the total rate of hospitalisation occurs on the inoculation date and is not distributed over time.
Increase in Disabilities versus Estimated Time Series of V-Safe Hospitalisation Events
Disabilities versus V-Safe Hospitalisations
The following charts illustrate the relationship between the increase in the disability rate in the Civilian Labor Force (aged 16-64) and the projected rate of hospitalisation events with 2+ days of lost work and 5+ days of lost work, based on the Covid-19 vaccination administration rate for the 19-64 age group, and using the assumptions listed above.
The chart on the left shows the time series of the change in disability rate from Feb-2021 to Nov-2022 for the Civilian Labor Force (red), and also the time series of the projected rates of hospitalisations (green) associated with 2+ or 5+ days of lost work.
The chart on the right shows the correlation between the rise in the disability rate since Feb-2021 with the projected cumulative rate of hospitalisation associated with 5+ days of lost work. The regression R2 is 92% which is evidence for a strong relationship. The slope coefficient is 0.82 which means that there are fewer hospitalisations with 5 or more days of lost work than the observed increase in disabilities since Feb-2021.
We should also note that performing the correlation of cumulative time series is misleading, and the R2 should not be taken as an indication of establishing a statistically significant relationship as both time series have autocorrelation.
Nevertheless, the charts below strengthen the case for a causal relationship between the Covid-19 vaccines and disabilities, since the projected hospitalisation events are events that could lead to disability. Additionally, under the reasonable assumptions stated above, the time series are shown to be of the same order of magnitude with each excess hospitalisation (of vaccinated individuals versus our assumed background rate) translating into 1.22=(1/0.82) disabilities as measured using BLS data. In other words, the rate of projected hospitalisation events with 5 or more days of lost work, appears to under-estimate recorded disabilities by about 22%.
The higher rate of increase in disabilities than the computed rate of hospitalisation events with 5+ days of lost work could be explained in several different ways, or by a combination of factors including:
By the definition of an a hospitalisation event with 5+ days of lost work being in general more extreme than the rate of disabilities reported by the individuals surveyed by the BLS. i.e. some disabilities reported by the individuals surveyed by the BLS may be caused by adverse events that do not require hospitalisation for at least 5 days.
The under-reporting of hospitalisation events in the V-Safe due to participants not updating the mobile app.
The adjustment factor applied to estimate the rate of hospitalisations associated with dose 2 inoculation relative to the background hospitalisation rate was projected using the ratio of Severe Adverse Events (Sev AEs) in vaccinated versus placebo participants in the Pfizer trial. This may not be an accurate model for estimating ‘excess’ hospitalisations.
Other factors that may cause excess disabilities in the Civilian Labor Force in a concurrent timeframe to the vaccine rollout.
By selecting hospitalisation events with an added criteria of 2+ days lost of work, the rate of rise in disabilities is about half the computed rate of hospitalisation events. The broader definition for a hospitalisation event allows us to hypothesise that approximately half of those hospitalisations might have resulted in a disability.
Estimated SAEs and Sev AEs Versus Computed V-Safe Hospitalisation Events
Serious and Severe Adverse Events Versus V-Safe Hospitalisations
The chart shows the time series of the change in disability rate from Feb-2021 to Nov-2022 for the Civilian Labor Force (left scale), and also the time series of projected rate of SAEs (see Part 5) and Sev AEs (see Part 6). The chart also shows the time series of V-Safe hospitalisation events with 2+ and 5+ days of lost work.
The rate of hospitalisation events with 5+ days of lost work is higher than the projected rate of SAEs but lower than the rate of projected Sev AEs. Additionally, the rate of hospitalisation events with 2+ days of lost work is substantially greater than the rate of Sev. AEs.
The rate of increase in disabilities; the hospitalisation events with 5+ days of lost work; and the computed time series of Sev AEs, are of similar order of magnitude, with the increase in disabilities lying between the other two.
Observations
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Our results provide a stronger case for establishing a causal relationship between disabilities and the Covid-19 vaccines. We produced time series of hospitalisation events following dose 2 of the Covid-19 vaccine using the V-Safe database which provides further validation of our previous work, using an independent measure of safety events. The computed time series of hospitalisation events track closely the time series of SAEs and Sev. AEs that were computed based on the rates estimated during the mRNA clinical trials.
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We also observe that the rate increase in disabilities lies in between the hospitalisation events with 5+ days of lost work and the computed time series of Sev AEs, with these three time series being of similar order of magnitude.
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If we consider that there is a logical relationship between possible Severe Adverse Events following vaccination and subsequent hospitalisations events (as defined above) then we would have expected to observe similar rates of hospitalisations and Severe or Serious Adverse events. Our results confirmed our prior suspicions.
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The coincidence of the rise in disabilities with the start of the Covid-19 vaccination rollout in conjunction with the similar magnitude of the rate of SAEs and Sev. AEs (from the clinical trials) and now with the rate of hospitalisation events inferred from the V-Safe database, provides even stronger evidence towards asserting causation.
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We believe that a comprehensive investigation needs to be performed, either in the form of new Phase III clinical trials for at least a 3-year period, or a program of forensic autopsies in a large sample of deceased individuals where the Covid-19 vaccines were not suspected as a co-factor for cause of death.
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