June 23, 2016

Or the other way around

It’s a useful habit, when you see a causal claim based on observational data, to turn the direction around: the story says A causes B, but could B cause A instead? People get annoyed when you do this, because they think it’s silly. Sometimes, though, that is what is happening.

As a pedestrian and public transport user, I’m in favour of walkable neighbourhoods, so I like seeing research that says they are good for health. Today, Stuff has a story that casts a bit of doubt on those analyses.

The researchers used Utah driver’s-licence data, which again included height and weight, to divide all the neighbourhoods in Salt Lake County into four groups by average body mass index. They used Utah birth certificates, which report mother’s height and weight, and looked at 40,000 women who had at least two children while living in Salt Lake County during the 20-year study period.  Then they looked at women who moved from one neighbourhood to another between the two births. Women with higher BMI were more likely to  move to a higher-BMI neighbourhood.

If this is true in other cities and for people other than mothers with new babies, it’s going to exaggerate the health benefits of walkable neighbourhoods: there will be a feedback loop where these neighbourhoods provide more exercise opportunity, leading to lower BMI, leading to other people with lower BMI moving there.   It’s like with schools: suppose a school starts getting consistently good results because of good teaching. Wealthy families who value education will send their kids there, and the school will get even better results, but only partly because of good teaching.


Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient See all posts by Thomas Lumley »