Three common mistakes in medical journalism

I love Gary Schwitzer, a former journalism professor at the University of Minnesota and a key advocate for better health and medical reporting at Schwitzer has a quick list of the most common mistakes reporters make when writing about medical science, and I think it's something that everybody should take a look at.

Why does this bit of journalism inside-baseball matter to you? Simple. If you know how journalists are most likely to screw up, you'll be less likely to be led astray by those mistakes. And that matters a lot, especially when it comes to health science, where people are likely to make important decisions based partly on what they read in the media.

The three mistakes:

Absolute versus relative risk/benefit data

Many stories use relative risk reduction or benefit estimates without providing the absolute data. So, in other words, a drug is said to reduce the risk of hip fracture by 50% (relative risk reduction), without ever explaining that it’s a reduction from 2 fractures in 100 untreated women down to 1 fracture in 100 treated women. Yes, that’s 50%, but in order to understand the true scope of the potential benefit, people need to know that it’s only a 1% absolute risk reduction (and that all the other 99 who didn’t benefit still had to pay and still ran the risk of side effects).

Association does not equal causation

A second key observation is that journalists often fail to explain the inherent limitations in observational studies – especially that they can not establish cause and effect. They can point to a strong statistical association but they can’t prove that A causes B, or that if you do A you’ll be protected from B. But over and over we see news stories suggesting causal links. They use active verbs in inaccurately suggesting established benefits.

How we discuss screening tests

The third recurring problem I see in health news stories involves screening tests. ... “Screening,” I believe, should only be used to refer to looking for problems in people who don’t have signs or symptoms or a family history. So it’s like going into Yankee Stadium filled with 50,000 people about whom you know very little and looking for disease in all of them. ... I have heard women with breast cancer argue, for example, that mammograms saved their lives because they were found to have cancer just as their mothers did. I think that using “screening” in this context distorts the discussion because such a woman was obviously at higher risk because of her family history. She’s not just one of the 50,000 in the general population in the stadium. There were special reasons to look more closely in her. There may not be reasons to look more closely in the 49,999 others.

Via The Knight Science Journalism Tracker