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The Statistical Secrets of Covid-19 Vaccines

Wired – “…The key here, though, is that Absolute Risk Reduction does change according to how at-risk the groups of people were in the first place. And this pandemic has widely varying risks across populations, and those change over time. (For example, viral variants change how infectious Covid can be, and young people’s risk of severe illness and death has changed as social policies and infection rates have fluctuated. It’s a hard problem!) I’m suggesting that this confusion, and the conflation of these two ideas, might be at the heart of some hesitance. By not being clear about the different flavors of risk and benefit for different vaccines and different people, public health experts have let doubt and dodgy personal interpretations flourish. Someone who’s hesitant to get a vaccination against Covid—not a full-on anti-vaxxer—might be worried about their own risks (of getting Covid-19 or vaccines), and unclear how they weigh against the benefit of almost certainly not getting Covid-19. Efficacy, or relative risk reduction, paints with too broad a brush—and brushes aside their personal assessments. “As individuals, we think of risk as ‘my individual risk.’ But the risk is a statistical calculation,” Olliaro says. Absolute risk helps clarify the individual-risk part. It also helps with policymaking, because it allows people with calculators to figure out exactly how many lives they’re going to save. To really bring that into focus, the inverse of the absolute risk reduction—1/ARR, if you’re even fractionally interested—is called the Number Needed to Vaccinate, or NNV. Which is to say: How many people do you have to vaccinate to prevent just one case of Covid-19 (or one severe case, or one death—depends on the study’s endpoints)?…

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