If you pick up the latest medical study, one of the first questions you're likely to wonder is whether the findings are significant. Do the numbers, percentages and ratios tell anything new?
As a healthcare journalist, it's a constant challenge to wade through a myriad of studies and determine what's worth reporting to readers. Do the numbers indicate a significant scientific finding or alert us to a potential risk--or not?
But I must admit I've now started looking at numbers somewhat differently after reviewing a new study from the Cochrane Database of Systematic Reviews on how the presentation of numbers can change public perceptions of risk and risk reductions.
It's not about tweaking findings or leaving out statistics that don't support a study's premise. Instead, it's about presenting findings in a way that the public, medical professionals and even health policymakers can better understand risk.
To best explain this, let's go to the numbers. Perhaps you have come across an article about a drug that cuts the risk of hip fracture over a three-year period by 50 percent. At first glance, that sounds incredible. That could be a major breakthrough.
But hold on a second. Let's break that down even more. What it may actually indicate is that 1 percent of people not taking the drug had fractures, while only 0.5 percent taking drug had fractures. The risk reduction now doesn't seem as impressive.
And by breaking that number down into people who are impacted--what the study authors call "the number needed to treat (NNT)"--the effect is expressed as one out of 200 people avoiding a hip fracture. It becomes a statistic that is easier for everyone to understand--and one that shows a vastly lowered risk.
The Cochrane researchers, when they reviewed data from 35 studies, found that participants in those studies understood frequencies [e.g., one out of 200 people] better. Relative risk reductions, such as in "the drug cuts the risk by 50 percent", seemed to be less well-understood. Ironically, participants perceived risk reductions to be inappropriately greater when using the people NNT ratio.
"People perceive risk reductions to be larger and are more persuaded to adopt a health intervention when its effect is presented in relative terms," said one of the study authors, Elie Akl of the University at Buffalo Department of Medicine. "What we don't know yet is whether doctors or policymakers might actually make different decisions based on the way health benefits are presented."
So, maybe now is the time to look at the numbers in terms of people impacted. At a time when healthcare costs are rapidly rising and outcomes are being more closely analyzed, let's make sure all of us--and not just healthcare researchers and analysts--can better determine what the risks really are. - Jan