Texas Attorney General Ken Paxton last week accused pharmaceutical giant Pfizer of “misleading the public” by “unlawfully misrepresenting” the effectiveness of its mRNA COVID-19 vaccine and trying to silence critics. He sued Pfizer for doing so.
The lawsuit also accuses Pfizer of failing to end the pandemic after the vaccine was released in December 2020. “However, contrary to Pfizer’s public statements, the pandemic has not ended and has gotten worse in 2021.” The complaint states:.
“We seek justice for Texans, many of whom are forced by overbearing vaccination mandates to take defective products sold to them under falsehoods,” Paxton said in a press release. . “The facts are clear: Pfizer did not tell the truth about its COVID-19 vaccine.”
Overall, Paxton’s 54-page complaint serves as a compendium of pandemic-era anti-vaccine misinformation and metaphors, while also making numerous unsubstantiated claims. But at the heart of the Lone Star State’s volatile legal debate is one that centers on the standard mathematics used by Pfizer to evaluate the effectiveness of its vaccine: relative risk reduction calculations.
This argument is both inaccurate and unoriginal. Anti-vaccine advocates have defended this flawed math-based theory since the height of the pandemic. Actual experts have thoroughly debunked it time and time again. Still, its absurdist glory was revealed last week in Paxton’s lawsuit seeking $10 million in damages.
mathematics argument
Simply put, this lawsuit and the anti-vaccine rhetoric that preceded it argue that Pfizer should have presented the vaccine’s effectiveness in terms of absolute risk reduction, rather than relative risk reduction. That would make a highly effective coronavirus vaccine appear extremely ineffective. As advertised by Pfizer, its vaccine appeared to be 95 percent effective at preventing COVID-19, based on relative risk reduction in a two-month trial. However, if you calculate the absolute risk reduction using the same trial data, the vaccine’s effectiveness is 0.85%.
The difference between the two calculations is very simple. Absolute risk reduction is a matter of subtraction. That is, the percentage point reduction in the risk of disease between the untreated and treated groups. So, for example, if a group of untreated people has a 60% risk of developing the disease, but receiving treatment reduces the risk to 10%, the absolute risk reduction is 50% (60 – 10 = 50).
Relative risk reduction involves a division, which is the difference in the rate of change between the two groups’ risks. So, as in the previous example, if treatment reduced the risk of disease from 60 percent to 10 percent, the relative risk reduction would be 83 percent (50 percent reduction / 60 percent initial risk = ~ 0.83).
Both numbers are useful when weighing the risks and benefits of treatment, but absolute risk reduction is especially important when the risk of disease is low. This is easy to understand by simply moving the decimal point in the example above. If treatment reduces a person’s risk of disease from 6 percent to 1 percent, the relative risk reduction is still 83 percent, which is an excellent number in favor of treatment. However, the absolute risk reduction is only 5%, and this can be easily counteracted by: Potential side effects and high costs.
