Last week, the internet was aflutter with news of a study showing how head injuries rose in cities with bike share—at least according to the media. But it turns out that most of us were making a major error in interpreting the data, as CityLab explains today. In fact, head injuries actually went down overall.
So how did so many wires get crossed? Here's what happened: The study published in the American Journal of Public Health looked at injury data in Montreal, Washington, D.C., Minneapolis, Boston, and Miami Beach, all cities with new bike share programs, both before and after the bike shares began. Then they did the same thing for five cities without bike shares.
What they found was that as a proportion of total injuries, head injuries went up in bike share cities. While that certainly isn't good, it's also not the whole story. While the proportion of head injuries might be on the rise, the injuries themselves are going down, as Eric Jaffe explains:
When Teschke reevaluated the core data — found in Table 2 of the AJPH report — she realized the injuries themselves had fallen in bike-share cities. Total injuries per year in these places decreased about 28 percent, and total head injuries decreased about 14 percent, she explained in a tweet last week.
So though the researcher's core argument—that helmets should be part of bike share programs—is definitely supported by their finding that head injuries increased in proportion to injuries overall, the issue of how bikes affect injury numbers is way more complicated than that.
After all, since injuries went down, you could make a counter-argument that as bikes on the road increase, drivers and cyclists get smarter and more experienced with each other—and accidents fall. Or even that bike share bikes are actually safer than average bikes, since they're regularly maintained and tend not to reach high speeds.
In other words, plenty of questions about how bike shares affect our cities remain unanswered. And as we learned through this little misunderstanding, it's all too easy to misinterpret data when we're in a hurry to prove a point. [CityLab]