A team of European researchers announced this week that they had determined that 16 percent of cancer cases worldwide (an estimated two million annually) are due to treatable infections, such as the human papilloma virus.
It won’t surprise you to learn that this report, from the International Agency for Research on Cancer in France generated a swarm of stories from around the world, more than 400 according to my Google news search. Most of the coverage, ranging from this CBS News story by Ryan Jaslow, to this Fox News story by Alex Crees tend to focus on the importance of prevention (vaccines) and treatment (effective antimicrobials). There was also this brisk statistical report from HealthDayNews, “Infection Causes 1 in 6 Cancers Worldwide” which was picked up by numerous outlets.
My own first reaction was to rather uncritically accept the study as more evidence for the importance of vaccination in public health. As I find myself perpetually exasperated by the anti-vaccine movement, I still like that idea. But I’m grateful to blogger Ed Yong this week for reminding me that even when one likes an idea, it still pays to think analytically about the science in question.
In a post Thursday at his blog, Not Exactly Rocket Science, Yong posed the question: What Does It Mean to Say That Something Causes 16% of Cancers? He acknowledges the well-established connection between some infections and some cancers but goes on to say:
But for me the reports aggravated an old itch. I used to work at a cancer charity. We’d get frequent requests for such numbers (e.g. how many cancers are caused by tobacco?) However, whenever such reports actually came out we got a lot of confused questions and comments. The problem is that many (most?) people have no idea what it actually means to say that X% of cancers are caused by something, where those numbers come from, or how they should be used.
Yong then takes the reader through a wonderfully clear explanation of the source of these calculations, called Population Attributable Fractions (PAFs), how they are derived (computer models and data crunching), and how to place these numbers in the context of reality. Or as he points out: Those two million cases don’t correspond to actual specific people. I can’t tell you their names.
Yong does emphasize that PAF models can do excellent job of giving public health workers a sense of the scope of a problem, identifying goals and targets in reducing disease incidence. He’s more critical of journalists who merely embrace the numbers without understanding them or providing their readers with a sense of context for such findings.
Well, I won’t merely repeat every point in his post here. But I will recommend that you read it. It will make you feel smarter. It certainly did that for me.
— Deborah Blum
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