In insurance, our business is to define, evaluate and protect against various forms of risk—a difficult task to be sure. And with commerce traveling at what would have seemed like light speed 50 years ago (thanks to the Internet), we depend on readily available numbers from researchers and analysts to help us make the right choices for ourselves and our customers.
We probably give very little thought to the veracity of such numbers, especially if they come from what we consider to be a trusted source. Yet a new research literature review (the Cochrane Systematic Review) suggests that health professionals and consumers may change their perceptions when the same risks and risk reductions are presented using alternative statistical formats.
According to the review, as described in a recent article in ScienceDaily, risk statistics can be used persuasively to present health interventions in different lights. “The different ways of expressing risk can prove confusing and there has been much debate about how to improve the communication of health statistics,” the article notes.
For example, the article says, one could read that a drug cuts the risk of hip fracture over a three year period by 50%. “At first sight, this would seem like an incredible breakthrough. In fact, what it might equally mean is that without taking the drug 1% of people have fractures, and with the drug only 0.5% do. Now the benefit seems to be much less.” The implications for health insurers here are obvious, since a less-than-careful reading of the “risk” could lead to insurers approving (and paying for) a drug that demonstrates minimal benefits.
The article adds that the statement of a 50% reduction is typically expressed as a Relative Risk Reduction (RRR). Saying that 0.5% fewer people will have broken hips, however, is an Absolute Risk Reduction (ARR). Saying that 200 people need to be treated to prevent one occurrence is referred to as the Number Needed to Treat (NNT). All these statistics are true, yet one of them is clearly misleading.
In the new study, Cochrane researchers reviewed data from 35 studies assessing understanding of risk statistics by health professionals and consumers. They found that participants in the studies understood frequencies better than probabilities. Relative risk reductions (the drug cuts the risk by 50%) were less well understood. Participants perceived risk reductions to be inappropriately greater compared to the same benefits presented using absolute risk or NNT.
Obviously, this kind of statistical confusion—or purposeful misstatement—is a danger to insurers of every stripe. If a study showed a 100% increase in hurricanes from one year to the next, it would obviously be important for a P&C insurer to realize that in year one there was one hurricane and in year two there were two. Yet we are constantly bombarded with risk “facts” and figures that purport to show something that a given source wants us to believe, while a deeper examination of the numbers would give the lie to one’s first perceptions.
The problem is that we are so busy and so overwhelmed with information overload that we tend to read the headlines and ignore the texts of the stories that interest us. And the headlines are deliberately sensational, because those who bring you the stories want to capture our attention. “100% Increase in Hurricanes Seen” is a lot more provocative than “Hurricane Frequency Increases by 1.”
In making any decision that involves interpreting research data, we must, then, take the time to understand what we are reading. It may keep us from hurrying on to the next tantalizing headline, but in the end it could save us from some embarrassing gaffes.
Ara C. Trembly (www.aratremblytechnology.com) is the founder of Ara Trembly, The Tech Consultant, and a longtime observer of technology in insurance and financial services.
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