News
Top blogs
Podcast
Topics
EventsThe American Obesity Epidemic Reaches a New Record
in The obesity epidemic
Five states moved to a higher category compared to 2013, none moved in the other direction. Compared to the 80’s it’s a brand new world – and not in a good way.
The simplistic “eat less, move more” message is still not working. Nor is sudden unexplained gluttony or laziness the cause of the epidemic.
Earlier
Obesity is “Exploding” in Europe, Except in This Country
Note
The methods behind the statistics changed a bit in 2010-2011. The surveys are based on telephone interviews and they had to change from landlines to cell phones. Thus the CDC discourages direct comparisons between the years prior to and after the change. However, it’s clear from the data in the years surrounding the change that the difference in methods had a relatively small impact – it’s hard to even notice it. It does not change the big picture.
Did the definition of obesity also change?
There's little doubt that the USDA MyPlateOfMetabolicSyndrome diet, and the hopelessly idiotic "eat less, move more" policies are superb at making and keeping people fat and sick, but the bungling bureaucrats at CDC are easily just as incompetent, if not as corrupt, as those at the USDA, and they will massage both the data and the definitions to further their agendas (which rarely have anything to do with actual public health).
No. It's a BMI>30 during the entire period.
http://www.cdc.gov/obesity/data/prevalence-maps.html
However, I think Dr. Eenfeldt is missing a huge opportunity for data gathering and analysis, and may not even be aware of its existence. What if you could see into the future, what if you could take a population of around 100,000 and see how these people look after years of over processed foods and junk food marketing? Not just to project data and make assumptions, but to have real valid data from real people? In America it would be easy to do. You only have to look at the Navajo Nation in Arizona/New Mexico, various articles reference a population where >60% have Type II Diabetes. The Navajo live on the reservation, a huge tract of land ~27,000sq miles. Very isolated but yet, plenty of fast food options. How is this different from analysis of data from Whites, Blacks, or Hispanics? Only Navajo can live on the reservation, there are no Whites, Blacks, or Hispanics, only Indians, giving you a land-locked nation.
I agree with you, though, about the collection techniques used for this. I never, NEVER answer a survey (online, by phone, otherwise). So, they're not getting data from me or people like me.
On the other hand, my wife loves surveys.
An individual, who had a BMI=23 at 25 years of age which has risen to a BMI=35 at 40 years of age, also has a serious problem going on.
I agree with you Bob that saying BMI=30 is some kind of magic number where problem starts is ludicrous. But that argument doesn't make BMI measurements useless.
The only way to obtain reliable statistics of obesity would be by adopting the criteria of body fat percentage (men equal or less 18% and women equal or less 25%) and obtaining a random sample from an accurately defined population of the USA and Puerto Rico. With a 99% confidence level and a 1% confidence inter val the sample size would be 16,640 out of a 320 million US population.
Sample subjects would have to be measured in person via DEXA machine, the most accurate method.
Independent observers would have to be present to vouch for the accuracy of the measurements.
I'll bet the house that the results would be much worse than they are now.
One that will be accurate and unquestionable. Your "rough benchmarks" produce inaccurate information which minimize the problem thus deceiving the public.
Taxpayers dollars should produce accurate results. Don't you research thoroughly an item you are considering buying to ensure it meets your specifications?
The money should be spend on education: Nutrition, How does the Body react to it, what should one eat, how to get good food on a low income, etc.
And there is a really good linear correlation between the rise in BMI numbers of a large enough example group, and obesity (and accordingly health).