Texas Attorney General Ken Paxton sued Pfizer last week, claiming the pharmaceutical giant “deceived the public” by “unlawfully misrepresenting” the effectiveness of its mRNA COVID-19 vaccine and sought to silence critics.
The lawsuit also blames Pfizer for not ending the pandemic after the vaccine’s release in December 2020. “Contrary to Pfizer’s public statements, however, the pandemic did not end; it got worse” in 2021, the complaint reads.
“We are pursuing justice for the people of Texas, many of whom were coerced by tyrannical vaccine mandates to take a defective product sold by lies,” Paxton said in a press release. “The facts are clear. Pfizer did not tell the truth about their COVID-19 vaccines.”
In all, Paxton’s 54-page complaint acts as a compendium of pandemic-era anti-vaccine misinformation and tropes while making a slew of unsupported claims. But, central to the Lone Star State’s shaky legal argument is one that centers on the standard math Pfizer used to assess the effectiveness of its vaccine: a calculation of relative risk reduction.
This argument is as unoriginal as it is incorrect. Anti-vaccine advocates have championed this flawed math-based theory since the height of the pandemic. Actual experts have roundly debunked many times. Still, it appears in all its absurd glory in Paxton’s lawsuit last week, which seeks $10 million in reparations.
Math argument
Briefly, the lawsuit and the anti-vaccine rhetoric before it argues that Pfizer should have presented its vaccine’s effectiveness in terms of absolute risk reduction rather than relative risk reduction. Doing so would have made the highly effective COVID-19 vaccine appear extremely ineffective. Based on relative risk reduction in a two-month trial, Pfizer’s vaccine appeared 95 percent effective at preventing COVID-19—as Pfizer advertised. Using the same trial data but calculating absolute risk reduction, however, the vaccine effectiveness would have been 0.85 percent.
The difference between the two calculations is quite simple: Absolute risk reduction is a matter of subtraction—the percentage point drop in risk of a disease between an untreated and treated group. So, for example, if a group of untreated people have a 60 percent risk of developing a disease, but, when treated, the risk drops to 10 percent, the absolute risk reduction is 50 percent (60 – 10 = 50).
Relative risk reduction involves division—the percent change difference between the two groups’ risks. So, as in the previous example, if a treatment lowers disease risk from 60 percent to 10 percent, the relative risk reduction is 83 percent (50 percent drop / 60 percent initial risk = ~0.83).
Both numbers can be helpful when weighing the risks and benefits of treatments, but absolute risk reduction is particularly key when there’s a low risk of a disease. This can be easily understood by simply moving the decimal in the above example. If a treatment lowers a person’s disease risk from 6 percent to 1 percent, the relative risk reduction is still 83 percent—an impressive number that might argue for treatment—but the absolute risk reduction is a paltry 5 percent—which could easily be negated by any potential side effects or high costs.
Calculation context
Similar calculations were seen in Pfizer’s trial: Of 17,411 vaccinated people, eight developed COVID-19 during the trial (0.045 percent), while 162 of 17,511 people in the placebo group developed the infection (0.9 percent). The relative risk reduction is 95 percent, while the absolute risk reduction is a decidedly unimpressive 0.85 percent.
For this reason, anti-vaccine advocates have seized upon absolute risk calculations as a way to downplay the effectiveness of lifesaving vaccines. But this steamrolls over the reason relative risk is used to examine vaccine effectiveness against infectious disease. Absolute risk is most helpful when it’s relatively stable and can be confidently calculated, such as calculating the risk of cardiovascular disease in a person, given their blood pressure and cholesterol levels. In these cases, absolute risk reduction is very helpful in evaluating the effectiveness of a treatment. But using absolute risk for infectious diseases is often simply nonsensical because absolute risk can vary dramatically by space, time, and many other factors.
For instance, a person’s absolute risk of flu drops significantly when it’s not flu season. Or, if a person hops on a plane, traveling from an area with no endemic malaria directly into an area with a raging malaria outbreak, their absolute risk of infection will skyrocket when they land. And, of course, if people are under lockdowns or health restrictions—as was the case early in the COVID-19 pandemic, when Pfizer’s vaccine trials were running—their absolute risk can rise significantly when those lockdowns and restrictions are lifted—as was the case in 2021 after the vaccine’s release.
With absolute risk so variable, relative risk is a more helpful measure of a vaccine’s effectiveness—and the relative risk reduction applies regardless of what a person’s absolute risk is at a given point in time.
Artificial numbers
Another key reason it’s safe to ignore absolute risk reduction in vaccine trial data is that it is often entirely artificial. These trials are designed to enroll large numbers of people with the full knowledge that only a small percentage will be exposed during the trial. And they’re designed with trigger points—in other words, when the trial hits a pre-determined number of infections, effectiveness is calculated.
In Pfizer’s trial, the trigger point was at or around 170. But, if the trial runners had set the trigger point somewhere else, the absolute risk reduction could easily have been different, even if everything else was the same. For instance, if it was set to 510 and there were 24 cases in the vaccine groups (0.13 percent) in the vaccine group and 486 cases in the placebo group (2.8 percent), the relative risk reduction would still be 95 percent, but the absolute risk reduction would be 2.67 percent, not 0.85 percent.
If Pfizer had waited for all 35,000 or so people in the trial to be exposed to COVID-19 amid widespread lockdowns, it would have needlessly delayed the evaluation and release of the vaccines.
An analysis from last year estimated that COVID-19 vaccines prevented more than 18 million hospitalizations and more than 3 million deaths in the first two years after their release.
In a statement to Stat, Pfizer noted the benefits and safety of its vaccine, saying that its representations of its vaccine “have been accurate and science-based. [Pfizer] believes that the state’s case has no merit and will respond to the petition in court in due course.”