An acclaimed study on the effectiveness of masks in reducing symptomatic COVID-19 is facing new scrutiny after a researcher highlighted the minuscule infection differences between “treatment” and control groups randomized across 600 Bangladeshi villages.
Accused of design flaws and overstating its findings when it was released in late August, the study’s newly released data show only 20 more symptomatic COVID cases in the villages that didn’t receive masks and related education, reminders and “role modeling by community leaders.”
In a total study population of 342,126 adults, 1,106 people in the control group tested positive, compared to 1,086 in the treatment group. The latter group represented 52% of the study population.
“I have a hard time going from these numbers to the assured conclusions that ‘masks work’ that was promulgated by the media or the authors after this preprint [not yet peer reviewed] appeared,” University of California Berkeley professor Ben Recht, who studies machine learning, wrote in an essay last week.
He said he was frustrated that the “raw number of seropositive cases” was left out of the preprint by researchers led by Yale University economists Jason Abaluck and Ahmed Mobarak, preventing him from “computing standard statistical analyses of their results.”
The researchers posted the replication code and data in early November, long after media coverage touting “the largest randomized trial to demonstrate the effectiveness of surgical masks, in particular, to curb transmission of the coronavirus.”
In light of the full release, “a complex intervention including an educational program, free masks, encouraged mask wearing, and surveillance in a poor country with low population immunity and no vaccination showed at best modest reduction in infection,” Recht said.
The newly provided raw numbers exacerbate other weaknesses of the study, according to Recht, who was also initially skeptical of the research because of its “statistical ambiguity.”
The study was not blinded, did not exclude pre-intervention infections, and was “highly complex” because of the mixed interventions, he said.
The three-percentage-point differential between household visit consent rates for the treatment and control groups, by itself, “could wash away the difference in observed cases,” he explained, adding that relative measures of risk are “[o]ne of the dark tricks of biostatistics,” which unlike hard case counts have a tendency to exaggerate effects.
‘How robust can this possibly be?’ …
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