Hacking at 0300

The Centers for Disease Control and the American Academy of Pediatrics just recommended that pediatricians stop using the old growth charts, based on 40 years of NHANES data, and switch to new charts from the World Health Organization, for children less than 2. The NHANES study is a survey of child health from the United States, and the original charts based on that data was descriptive: the charts showed what the distribution of heights and weights was for American children. That was used by pediatricians to define "normal," to say whether you were overweight or underweight. With the 2000 charts, they decided that kids were too heavy and that we couldn't call that "normal" anymore. So they effectively threw out all the recent weight data, and made the charts based on the 1970 distribution of weight (see page 16 of the original paper) and made the charts partly prescriptive (telling us what they thought the distribution of weight should be) and partly descriptive.

Now they've gone all the way: the WHO explicitly followed an international group of babies who were "were raised in environments that promote healthy growth", including exclusively breastfeeding to 4 months and continuing breastfeeding to 12 months, no smoking, and rich enough to be able to eat well.

I updated the bililite webservices charts to reflect the new charts, but I'm of two minds about the change. As a pediatrician following a kid's weight, I really do want to compare it to "healthy" rather than "common," so the change is for the better. On the other hand, it enshrines assumptions about what "healthy" means without good evidence that children who fall in the prescribed ranges actually live longer, healthier lives. We will get more recommendations for further restricting our children's lives in order to force them onto these graphs.

As these charts get more use, look for more press about increasing obesity, as we redefine the percentile lines downward, pushing more kids over the "overweight" 85^th percentile. It's like the AMT of biostatistics!