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Accidents Way Up After Jabbings

from ACD

I’d do a forensic-quality autopsy on the data — the US Fatality Analysis Reporting System (FARS) and a couple state-level databases — to determine more definitively which possible concerns prove causal.  Already we have preliminary data showing changes in annual data temporally corresponding to one major feature of 2020-2021.


VACCIDENTS: US traffic fatalities SKYROCKETED across the board in 2020 and 2021 – reaching highest level in decades, according to Federal Agency

As it is, the leading hypothesis — mRNA experimental injections leading to “sudden and unexpected deaths”, especially in age groups not prone to to such —

for example,


So many people are now “dying suddenly” that “our free press” can barely hide it any more

SHOCKING: UK Government admits COVID Vaccinated Children are 4423% more likely to die of any cause & 13,633% more likely to die of COVID-19 than Unvaccinated Children

also has associated an immense array of adverse “side effects” due to compromising the human immune system, some of which will likely compromise a “vaxxinated” driver’s ability in many different ways.

Hence the challenge to the Data Analyst | Statistical Modeler, especially one re-emerging from quasi-retirement, accepting the cudgel, wading into oceans of 1’s and 0’s, the quiver packed with statistical software, the bow of numerical truth at the ready, an empiricist seeking lairs of relativistic enemies.

Surely the epidemiology of sports-related injuries and deaths seems a lot friendlier!  I think it much simpler to count dead bodies on playing fields, deaths witnessed by thousands of on-lookers — and often accurately reported!  Given the rarity of such shocking events in past decades, current counts would offer an unmistakable  2-sigma (even a 6-sigma) signal.

Teasing out a contemporary signal from motor vehicle accident data may prove unproductive fun — not that I have aversion to passing time enjoyably and without purpose!  Old age does have its benefits.

One reply on “Accidents Way Up After Jabbings”

Epitaph for this empiricist: “There are no accidents in the Universe,” replacing the old saw about GOD playing dice.

Coinky-dinks, however, abound. For example:

An investigation of official statistics has found that the number of athletes who have died since the beginning of 2021 has risen exponentially compared to the yearly number of deaths of athletes officially recorded between 1966 and 2004.

So much so that the monthly average number of deaths between January 2021 and April 2022 is 1,700% higher than the monthly average between 1966 and 2004, and the current trend for 2022 so far shows this could increase to 4,120% if the increased number of deaths continues, with the number of deaths in March 2022 alone 3 times higher than the previous annual average.

For those of you still bothered by — or harassed by — the hoary notion “Correlation does not necessarily imply causation,”, consider the seminal work of Rosenbaun and Rubin, “The central role of the propensity score in observational studies for causal effects,” Biometrika. 70 (1983) 41-55.

The flip side of the statistical coin: “Causation necessarily implies correslation.” As I did in the arena of “motor vehicle risk analysis”, wherein “accident causation” proved NOT an oxymoron, one can apply Propensity Score Analysis to good advantage with existing datasets underpinning studies “criticized” or “labelled” CORRELATIONAL. Transforming a pig’s ear to a silk purse has never seemed simpler, in my opinion! If this comment board supports even more verbiage than the preceding, I provide an abstract of a study (2004) I presented to the Society for Risk Analysis.

Motor vehicle collisions are complex events involving multiple factors related to drivers, driving environments, and vehicles, a truism often belied by statistical estimations of risks of real-world crashes and their consequences. Many published studies feature logistic regression analyses of police-reported (observational) data on crashes and appear prone to biases due to omitted relevant variables and failure to deal effectively with confounding. In most cases, however, analysts ask seemingly simple, cause-and-effect questions. For instance, did this change in design, or would this change in condition, mitigate the target problem? The goal is neither to model the complexity of“the problem”, nor to assess the importance of a certain risk factor relative to other factors. Other risk factors, however influential or relevant, can be considered nuisance factors. In such cases, propensity score analysis (PSA) may well be the method of choice.The purpose of this paper is to foster a greater appreciation of the utility of PSA for motor vehicle risk analysis. Two applications of PSA are (1) assessing the effect of occupant loading on the risk of rollover of 15-passenger vans; and (2) evaluating the effect of retroreflective tape on heavy trailers for the risk of side or rear impact by other vehicles during hours of darkness. Results of PSA indicated lesser effects of putative causes (higher numbers of passengers, presence of retroreflective tape) than claimed in published studies to date. In both instances, differences in the magnitude of effects were due mostly to the influence of omitted and confounding variables. Applying PSA, analysts still have to do thorough exploratory data analyses. For the preliminary assessment of simple cause-and-effect relationships, however, analysts can concern themselves much less about the structure and complexity of motor vehicle crashes and their outcomes.

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