Sunday, October 17, 2010

New call to tax junk food

No clear evidence quoted to show that the proposed taxes will have any effect on obesity. You know why? Because there IS NO such evidence -- none that I have seen after years of reading in the literature anyway. If you tax a particular food, it is basic economics that people will switch their preferences to other foods. People will just get the calories they want elsewhere. And a tax on salty food would be a real laugh. Are they going to confiscate all salt-shakers as well?

The article below makes vague reference to a study of salty foods that supports their case so I looked up the MJA to get the exact reference but the latest issue does not contain the article referenced. Maybe it has not yet been put online. I'm betting that the "study" concerned was either a simulation or the usual epidemiological crap -- such as this. Only a double blind study would settle the matter

More public health experts have joined the call for a tax on junk food, saying the existing focus on "individual behaviour change" will do little to curb surging rates of obesity. Ms Holly Bond, PhD candidate at the Michael Kirby Centre for Public Health and Human Rights, said more than 60 per cent of Australian adults and one in four children were now either overweight or obese.

Obesity had overtaken smoking as the leading cause of premature death and illness, and yet government had so far resisted calls to adopt the same approach it championed for tobacco and alcohol. "Junk foods have the same pattern of misuse and the same social costs as tobacco and alcohol," said Ms Bond, from Monash University. "... We propose that a tax on junk food be implemented as a tool to reduce consumption and address the obesity epidemic."

Ms Bond said while many foods were high in sugar, salt and fat the tax could be applied to the "worst foods" in this category, those which had "little to no nutritional value such as potato chips, confectionary and soft drinks".

In an paper published in the latest edition of the Medical Journal of Australia, she points to US research which found a 10 per cent increase in soft drink prices would reduce consumption by 8 - 10 per cent. Another study found a 10 per cent increase in the price of salty snacks could reduce a typical American’s body weight by up to half a kilogram per year, and generate $1 billion. [So salt makes you fat???] It also found a 10 per cent reduction in fruit and vegetable prices, subsidised using junk food tax revenue, would increase their purchase by 7 and 5.8 per cent respectively.

"Unsurprisingly, the US soda industry, which claims that such taxes would ‘hurt hard working, low and middle income families, elderly residents and those living on fixed incomes‘ would destroy jobs," Ms Bond said. "Arguments of this sort were raised by the tobacco industry when tobacco taxation was first proposed."

Ms Bond said the companies involved in the sale of junk food had a responsibility to their shareholders first and so would "resist any change" that could hurt profits even those "for the greater public good".

She said the Henry tax review emphasised the importance of tobacco taxes in reducing smoking (but it ruled out a junk food tax) while a recent National Preventative and Health Taskforce report did recommend a review of tax policy to encourage healthier eating. "The government will more likely continue to construct obesity as a problem of individual behaviour change rather than one requiring comprehensive interventions," Ms Bond said. "This approach aligns with industry objectives and ... the results will probably be the softest forms of regulation, such as voluntary targets."

Imposing a 10 per cent tax on all junk food was also a central plank of the ACE-Prevention report, released last month and backed by the Public Health Association of Australia.


Why So Many (Medical) Studies Based On Statistics Are Wrong

W. Briggs, statistician, nails just one of the frauds that pervade medical research

This was inspired by the (unfortunately titled) article Lies, Damned Lies, and Medical Science, publishing in this month’s Atlantic (thanks A&LD!).

The article profiles the work of John Ioannidis, who has spent a career trying to show the world that the majority of peer-reviewed medical research is wrong, misleading, or of little use. Ioannidis “charges that as much as 90 percent of the published medical information that doctors rely on is flawed…he worries that the field of medical research is so pervasively flawed, and so riddled with conflicts of interest, that it might be chronically resistant to change—or even to publicly admitting that there’s a problem.”
“The studies were biased,” he says. “Sometimes they were overtly biased. Sometimes it was difficult to see the bias, but it was there.” Researchers headed into their studies wanting certain results—and, lo and behold, they were getting them. We think of the scientific process as being objective, rigorous, and even ruthless in separating out what is true from what we merely wish to be true, but in fact it’s easy to manipulate results, even unintentionally or unconsciously. “At every step in the process, there is room to distort results, a way to make a stronger claim or to select what is going to be concluded,” says Ioannidis. “There is an intellectual conflict of interest that pressures researchers to find whatever it is that is most likely to get them funded.”

Most medical studies—and most studies in other fields—rely on statistical models as primary evidence. The problem is that the way these statistical models are used is deeply flawed. That is, the problem is not really with the models themselves. The models are imperfect, but the errors in their construction are minimal. And since (academic) statisticians care primarily about how models are constructed (i.e. the mathematics), the system of training in statistics concentrates almost solely on model construction; thus, the flaw in the use of models is rarely apparent.

Without peering into the mathematical guts, here is how statistical studies actually work:

1. Data are gathered in the hopes of proving a cherished hypothesis.

2. A statistical model is selected from a toolbox which contains an enormous number of models, yet it is usually the hammer, or “regression”, that is invariably pulled out.

3. The model is then fit to the data. That is, the model has various drawstrings and cinches that can be used to tighten itself around the data, in much the same way a bathing suit is made to form-fit around a Victoria’s Secret model.

4. And to continue the swimsuit modeling analogy, the closer this data can be made to fit, the more beautiful the results are said to be. That is, the closer the data can be made to fit to the statistical model, the more confident that a researcher is that his cherished hypothesis is right.

5. If the fit of the data (swimsuit) on the model is eye popping enough, the results are published in a journal, which is mailed to subscribers in a brown paper wrapper. In certain cases, press releases are disseminated showing the model’s beauty to the world.

Despite the facetiousness, this is it: statistics really does work this way, from start to finish. What matters most, is the fit of the data to the model. That fit really is taken as evidence that the hypothesis is true.

But this is silly. At some point in their careers, all statisticians learn the mathematical “secret” that any set of data can be made to fit some model perfectly. Our toolbox contains more than enough candidate models, and one can always be found that fits to the desired, publishable tightness.

And still this wouldn’t be wrong, except that after the fit is made, the statistician and researcher stop. They should not!

Consider physics, a field which has far fewer problems than medicine. Data and models abound in physics, too. But after the fit is made, the model is used to predict brand new data, data nobody has yet seen; data, therefore, that is not as subject to researcher control or bias. Physics advances because it makes testable, verifiable predictions.

Fields that make use of statistics rarely make predictions with their models. The fit is all. Since any data can fit some model, it is no surprise when any data does fit some model. That is why so many results that use statistical models as primary evidence later turn out to be wrong. The researchers were looking in the wrong direction: to the past, when the should have been looking to the future.

This isn’t noticed because the published results are first filtered through people who practice statistics in just the same way.
Though scientists and science journalists are constantly talking up the value of the peer-review process, researchers admit among themselves that biased, erroneous, and even blatantly fraudulent studies easily slip through it. Nature, the grande dame of science journals, stated in a 2006 editorial, “Scientists understand that peer review per se provides only a minimal assurance of quality, and that the public conception of peer review as a stamp of authentication is far from the truth.” What’s more, the peer-review process often pressures researchers to shy away from striking out in genuinely new directions, and instead to build on the findings of their colleagues (that is, their potential reviewers) in ways that only seem like breakthroughs…

Except, of course, for studies which examine the influence of climate change, or for other studies which are in politically favorable fields: stem cell research, AIDS research, drug trials by pharmaceuticals, “gaps” in various sociological demographics, and on and on. Those are all OK.

Incidentally, predictions can be made from statistical models, just like in physics. It’s just that nobody does it. Partly this is because of expensive (twice as much data has to be collected), but mostly it’s because researchers wouldn’t like it. After all, they’d spend a lot of time showing what they wanted to believe is wrong. And who wants to do that?


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