Lies, damn lies and statistics.
Now to be fair I've been primed to disbelieve most future predictions by being mid-way through Dan Gardner's excellent Future Babble, but really, obesity rates to hit 42% is headline news?
The headlines referred to a study published last week in PLoS Computational Biology that had some truly fancy Harvard folks hammer out a formula to predict what obesity's going to do down the road. Those fancy folks are building on a prior study of theirs that proved that obesity is socially contagious and this one takes that one to it's apparently logical mathematical landing point of 42% of everyone you know's going to be obese one day.
Of course obesity is a highly complicated condition. Yes, it's simple to describe, more energy goes in than out, but ultimately there are a great many variables at play which impact on intake and output.
This most recent study doesn't appear to me to address any of those. For instance off the top of my head I would have thought it important in a study of prognostication to provide fancy statistical ways to explain why it wouldn't matter to outcomes if portions sizes continued to grow in restaurants, advertisers continue to ramp up their targeting of children, food delivery becomes even more ubiquitous, incentive or disincentive taxation schemes were enacted, or if suddenly our governments stopped subsidizing the base ingredients that allow food manufacturers to make calories insanely cheap, but hey, I admit quite readily, I'm no mathematician.
More importantly I've got to ask, "So what?". Arguing about how high obesity rates are going to climb is about as useful to helping the problem as folks on the Titanic arguing about exactly how big that iceberg is that's looming on the horizon.
I realize that basic research is important and I admit that I've been set off more by the news coverage than by the study, but at the end of the day what I'm trying to say is that while I'm sure the intellectual exercise of guesstimating how high obesity rates can climb was personally rewarding for those researchers, I can't help but wish that instead they'd have used their massive collective brainpower to work on something that actually has even the remotest bit of clinical relevance.
Hill, A., Rand, D., Nowak, M., & Christakis, N. (2010). Infectious Disease Modeling of Social Contagion in Networks PLoS Computational Biology, 6 (11) DOI: 10.1371/journal.pcbi.1000968
[thanks to Idea Sandbox for the graphic up above]