The possibility that this BMI shift is a statistical artifact is certainly troubling. How could we confirm it? Would it be possible to, say, run a modern study using the 1960s sampling technique, to try to isolate the time variable from the sampling-technique variable?

[(myl) I believe that there's pretty good demographic metadata for each subject, and therefore it ought to be possible to distinguish genuine trends from changes in the sampling procedures. It's possible and even likely that CDC biostatisticians have done exactly that, but I have been able to find documentation yet.]

]]>The wikipedia article

http://en.wikipedia.org/wiki/Body_mass_index

gives quite a good summary. It's a kludge, rather than a thought-through scientific assessment.

Those Belgian polymaths have a lot to answer for.

]]>[(myl) "Weight in kilograms divided by height in meters squared" is a definitional quotation from the authoritative source, C.L. Ogden & K.M. Flegal, "Changes in terminology for childhood overweight and obesity", National Health Statistics Reports 2010. What they (and I) obviously mean by this phrase is, adding parentheses to help you parse it, "(weight in kilograms) divided by ((height in meters) squared)". If you don't like the left-branching construction "height in meters squared", please take it up with the authors of the 15,700 scientific papers that Google Scholar indexes as containing this phrase.]

That said, your post illustrates one of the many problems that plague epidemiology. Too many pronouncements are made on flawed data or ignorance of confounding factors.

]]>[(myl) An interesting and relevant argument. But the history I quote suggests that the sampling procedures for non-Hispanic whites might have changed as well: "The study respondents include whites as well as an oversample of blacks and Mexican-Americans. The study design also includes a representative sample of these groups by age, sex, and income level." It seems suspicious that the measure's distribution makes such a radical shift, precisely during the period when new sampling procedures were adopted.]

***

Per the 2010 census, 63.7% of the US population is non-Hispanic white. The 2009-2010 NHANES survey included 4420 non-Hispanic white in their sample of 10537 — 41.9%.

17% of the sample is above the 95th percentile of older samples. If this excess is purely due to oversampling, then we can estimate the obesity rate of the oversampled population:

41.9% / 63.7% is 65.8%, so roughly 66% of the sample matches the population race/ethnicity distribution. The remaining 34% of the sample represents the oversampling of Hispanic or non-white populations.

If the 66% sample matches the pre-1980s base data, 5% of this sample should be above the 95th percentile of the old data.

If the whole sample is 17% obese and 66% of the sample is only 5% obese, then the remaining 34% of the sample must be 41% obese.

But, we can then apply the same math to the population-matched sample. If the whole population-matched sample is only 5% obese and the Hispanic or non-white sample is 41% obese, then the non-Hispanic white sample (63.7% of the 66% population-matched sample) must be some -13% obese (negative 13%).

As a negative population percentage is unsound, oversampling cannot be the sole contributor to the excess obesity.

***

It looks like the true rate of obesity must be at least 10% in the 2009-2010 era, and that requires a huge differential in the rates of obesity between the non-Hispanic white population and the Hispanic or non-white populations.

10% population-matched obesity and 17% sample obesity => 31% oversample obesity.

31% oversample obesity and 10% population-matched obesity => 0% non-Hispanic white obesity.

Not plausible, but not mathmatically unsound.

The population rate needs to be about 12% to permit the non-Hispanic white obesity rate to be 5%, with the Hispanic or non-white rate then being 27%. Note that this outcome could mean that the (measured) obesity epidemic reflects both a measurement artifact (oversampling) and a demographic change (growth of "minority" populations).

If the population rate is above 12%, then the non-Hispanic white obesity rate must have increased above the 5% historical definition.

For example, a 14% population obesity could reflect as low as a 10% white obesity and as high as 23% minority obesity.

Choosing the 95th percentile from the 1960's just seems plain arbitrary if there's nothing special about that percentile and nothing particularly valuable about the weight distribution of that time period.

That said, it's not surprising that folks would grasp to a methodological change that resulted in increased obesity rates and not look below the surface to see what caused it. It's a modern day moral panic, and what's a moral panic without a few sky-is-falling statistics?

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