Causality, correlation, VAERS & Vaccines
Principles of correlation vs causation have to be applied when adding in the outcome of a vaccine. The context is people are using VAERS and the UKs similar system in Yellow Card Scheme and saying a vaccine has caused something without any other scientific evidence.
People misinterpret VAERS data without first applying the above principles that have to be used in these situations for vaccine adverse reactions, events and death. Especially the last two, causal relationships need to be tied using confounding variables to see what may have been the cause.
➡ What is epidemiology?
Is the study of the distribution and determinants of health-related states or events in specified populations and the applications of this study to control health problems. (Source John M. Last 2000, A Dictionary of Epidemiology: Fourth Edition, p. 62)
➡ What is correlation and causality?
Correlation is when something is linked, causality is the relating of causes by the effects they produce (Source John M. Last 2000, A Dictionary of Epidemiology: Fourth Edition, p. 26), how we can establish a causal relationship is by using causal diagrams between variables in order to factor in non-causal and causal associations.
Unfortunately, people will come to uninformed and unscientific conclusions. Mainly because of bias, this can constitute a threat to the evaluation of causal relationships. (Source: Moyses Szklo, F. Javier Nieto 2000, Epidemiology Beyond the Basics, Chapter 5, Identifying Noncausal Associations: Confounding; p. 209)
Medical experts, scientists and doctors understand the fear someone has when a report is made, same for VAERS, they are not dismissed, anecdotes and hearsay accounts are unreliable and not classed as scientific evidence. In science, this account cannot be used to adopt a path of causal plotting relationships.
➡ Example of correlation
One famous example of correlation is this. Does an increase in ice cream sales, increase the uptake of shark attacks? No, why? Because there is a correlation between the ice cream sales and shark attacks, but this doesn’t mean there’s a causal relationship between an increase in ice cream sales and more shark attacks, the causal evidence would be the temperature going up as this causes more people to buy ice cream and more people to swim in the water.
One fantastic website for understanding correlations. (Source: https://www.tylervigen.com/spurious-correlations)
➡ Scientific explanations of correlation and causality in Epidemiology
Correlation in epidemiology: The evidence generated by most epidemiological studies is correlational which, although potentially powerful, cannot be presumed to be causal. It does identify ‘risk factors’, and so the concept of correlation is an important one to understand. (Source: https://www.open.edu/openlearn/health-sports-psychology/health/epidemiology-introduction/content-section-2.1.2#:~:text=Activity%202%20Plotting%20correlations&text=The%20evidence%20generated%20by%20most,an%20important%20one%20to%20understand)
Causation in epidemiology: Epidemiology has a vested interest in causation as, despite its numerous and often vague definitions, it is a discipline with the goal of identifying causes of disease (both modifiable and nonmodifiable) so that the disease or its consequences might be prevented. (Source: https://www.ajo.com/article/S0002-9394(10)00486-1/fulltext)
➡ What is VAERS?
VAERS (Vaccine Adverse Events Reporting System) and similar systems are self-reporting systems that act as an early warning sign to spot possible safety problems. The main point of these systems is to identify trends. If 100 people report X, that’s a trend. If one person reports Y, that’s likely not related to the vaccine. The system is very flawed as anyone can make a report, Dr James Laidlaw purposely made a report to highlight this flaw of the system. (Source: https://books.google.co.uk/books?id=tWRKH-bz6qgC&pg=PA106&redir_esc=y#v=onepage&q&f=false)
These are some very important quotes that come directly from VAERS:
“Note that the inclusion of events in VAERS data does not infer causality.” — https://vaers.hhs.gov/data/datasets.html
“The number of reports alone cannot be interpreted or used to reach conclusions about the existence, severity, frequency, or rates of problems associated with vaccines.” — https://vaers.hhs.gov/data.html
“It is generally not possible to find out from VAERS data if a vaccine caused the adverse event VAERS data cannot be used to determine rates of adverse events”
“VAERS accepts reports of adverse events following vaccination without judging the cause or seriousness of the event. VAERS is not designed to determine if a vaccine caused an adverse event” — https://vaers.hhs.gov/faq.html
💥 So what are better systems than VAERS?
Well, there are two more reliable systems out there which medical experts use to plot better causal relationships to say if a vaccine was the causing factor of an outcome.
One being the VSD (Vaccine Safety Datalink) as medical records are used as well to help in plotting relationships. (Source: https://www.cdc.gov/vaccinesafety/ensuringsafety/monitoring/vsd/index.html).
This system also helped regarding a study on Adverse events surveillance which was done on over 11.8million people in the science journal JAMA. (Source: https://jamanetwork.com/journals/jama/fullarticle/2784015)
The other system that works very well as everything is all verified, is the CDC’s Morbidity and Mortality Weekly Report. (Source: https://www.cdc.gov/mmwr/index.html)
➡ Differences between side effects, adverse reactions and adverse events
Side effect: Problems that occur when treatment goes beyond the desired effect.
Adverse Reaction: An unwanted effect caused by the administration of a drug
Adverse Event: Unwanted and usually harmful outcomes.
(Sources: https://www.medicinenet.com/side_effects/definition.htm
https://www.medicinenet.com/adverse_reaction/definition.htm
https://www.medicinenet.com/adverse_event/definition.htm)
Please do not misinterpret the above, in regards to vaccine outcomes, as causal relationships still need to be plotted in order to look at non-causal and causal associations whilst using confounding to establish this as well. Other factors have to be looked into such as ecological factors (geopolitical, environmental and cultural), health factors, lifestyle and diet choices, medical history, religious factors and so on, so the conclusion of causality is reliable.
💥 Thanks for reading, Lawrence. Please consider a small contribution, in the form of a beer as all articles are created in my small amount of spare time: https://www.buymeacoffee.com/LawrenceRob