Truth We Won’t Admit: Drinking Is Healthy

The evidence that abstinence from alcohol is a cause of heart disease and early death is irrefutable—yet this is almost unmentionable in the United States. Even as health bodies like the CDC and Dietary Guidelines for Americans (prepared by Health and Human Services) now recognize the decisive benefits from moderate drinking, each such announcement is met by an onslaught of opposition and criticism, and is always at risk of being reversed.

Noting that even drinking at non-pathological levels above recommended moderate limits gives you a better chance of a longer life than abstaining draws louder protests still. Yet that’s exactly what the evidence tells us.

Via Truth We Won’t Admit: Drinking Is Healthy – Pacific Standard: The Science of Society.

NYTimes: Low Carb Eaters Lose More Fat, Show Better Health

People who avoid carbohydrates and eat more fat, even saturated fat, lose more body fat and have fewer cardiovascular risks than people who follow the low-fat diet that health authorities have favored for decades, a major new study shows.

The new study was financed by the National Institutes of Health and published in the Annals of Internal Medicine. It included a racially diverse group of 150 men and women — a rarity in clinical nutrition studies — who were assigned to follow diets for one year that limited either the amount of carbs or fat that they could eat, but not overall calories.

By the end of the yearlong trial, people in the low-carbohydrate group had lost about eight pounds more on average than those in the low-fat group. They had significantly greater reductions in body fat than the low-fat group, and improvements in lean muscle mass — even though neither group changed their levels of physical activity.

While the low-fat group did lose weight, they appeared to lose more muscle than fat.

By the end of the yearlong trial, people in the low-carbohydrate group had lost about eight pounds more on average than those in the low-fat group. They had significantly greater reductions in body fat than the low-fat group, and improvements in lean muscle mass — even though neither group changed their levels of physical activity.

While the low-fat group did lose weight, they appeared to lose more muscle than fat.

In the end, people in the low-carbohydrate group saw markers of inflammation and triglycerides — a type of fat that circulates in the blood — plunge. Their HDL, the so-called good cholesterol, rose more sharply than it did for people in the low-fat group.

Those on the low-carbohydrate diet ultimately did so well that they managed to lower their Framingham risk scores, which calculate the likelihood of a heart attack within the next 10 years. The low-fat group on average had no improvement in their scores.

Score another one for Atkins, Taubes, et al. Note also that this is a direct refutation of the Food Pyramid the Federal government has been shoving down our throats for years. What else have they gotten wrong? Via A Call for a Low-Carb Diet – NYTimes.com.

Potentially “Lethal Blow” For Obamacare: Subsidies Invalid In Most States

in what NBC classified as a "potentially lethal blow to Obamacare" a federal appeals court has ruled that the federal government may not subsidize health insurance plans bought by people in states that decided not to set up their own marketplaces under Obamacare. The law clearly says that states are to set up the exchanges. But 34 states opted not to, and the federal government took over in those states. The court ruled that federal government may not pay subsidies for insurance plans in those states.

I have to say I am partial to anything that has the potential to consign Obamacare to the ash heap of history. Maybe after that we can get something like market reforms into place, in terms of “real” insurance and published prices, instead of the perverse-incentives payment-plan system we have now. Via In Potentially "Lethal Blow" For Obamacare, US Appeals Court Finds Insurance Subsidies Invalid In Most States | Zero Hedge.

Birth Control Mandate Waivers For Me, But Not For Thee

Some 204 outfits favored by Democrats were granted waivers by the president from ObamaCare, which means their employees do not have the right to employer provided birth control. These include upscale restaurant, nightclubs, and hotels in then-Speaker Pelosi’s district; labor union chapters; large corporations, financial firms, and local governments.

Women did not march through the streets to complain on behalf of their downtrodden sisters at Boboquivari in San Francisco which sells porterhouse steaks at $59 a pop and such. Apparently they are up with laws written on Etch-a-Sketch boards which the president can rewrite at whim. And their moral outrage is dependent on whether or not the employer is a Democrat crony.

via Articles: Let's Build a Stairway to Alberta.

Don’t Confuse Money Flows With Real Resources

While it is true that we spend more than other countries [on medical care] in an accounting sense, we actually use fewer real resources: fewer doctors, fewer nurses, fewer hospital beds, shorter lengths of stay, etc. That means that from an economist’s point of view, we aren’t necessarily spending more than other countries.

Fuchs says that with an extra $1 trillion, we could have more bridges, more highways, more teachers, more R&D, etc. But once again, this confuses money flows with real resource use. We can’t devote more real resources to non-health care unless we use fewer real resources in health care. But if we copy other countries, the resource flow will go in the opposite direction. That is, in order to have more doctors, nurses, hospital beds, etc., we will have to have fewer teachers, fewer roads, less R&D!

via A Better Way to Save $1 Trillion | John Goodman's Health Policy Blog | NCPA.org.

Untangling Obamacare’s Web Glitches

What the heck could be going on? My friend stated the obvious: “It’s clear that they’re getting more traffic than they can handle. The question is why they can’t handle the traffic they’re getting.” Load problems could explain servers hanging in California and New York … but the drop-downs? The standard explanation for this is “high load,” but high server loads don’t cause your security dropboxes to empty out.

“The drop-down thing is mystifying,” he told me. If federal exchanges decided to populate the security question fields by calling up a list of possible questions from another server — one that didn’t have a lot of capacity — then that might be causing the sign-up process to stall at that step. For an application that expects a lot of traffic, this is a very bad idea.

“Just cache them on the front ends, for heaven’s sake, so you only need to ask once,” he said. “A database call to get questions shouldn’t be in the critical serving path. If you’re hitting the database just to load the security questions, then just serving individual pages is going to be expensive.”

The various glitches, he pointed out, “could very easily be because deadline pressure caused them to take some shortcuts that impacted their ability to scale.”

Such as?

“The aforementioned let’s-hit-the-database-for-security-questions thing.”

Why would they use such a seemingly obvious poor design?

“It can be easier to make a call to another server to get something when you need it than to implement a cache that you prepopulate either from static files or from the database on startup. Making a call to another server is also something you’d naturally think to do if you hadn’t had to focus on scalability before. The security question page is probably not the thing you’re most concerned about, so you give it to the new hire to do as their starter project. They don’t know what they’re doing, so they implement it the straightforward way … and since you’re under unbelievable deadline pressure to get something working now nobody reviews it in detail.”

Obviously, we don’t know if this theory is correct — but it does fit the particulars.

Government programmers are subject to the same development pressures as the rest of us. Via Untangling Obamacare’s Web Glitches – Bloomberg.

The Obamacare employer mandate delay is another symptom of our crumbling ‘rule of law’

I am not a legal scholar, so I need someone to explain it to me. In what sense do we live under the rule of law if the Congress can pass a bill, the president can sign it, and then the president can unilaterally announce that it is not going to be implemented as planned? Telling me that this kind of discretionary power is routinely exercised by the executive branch is not an answer. In what sense is legislation that permits such discretion the “rule of law”? Isn’t the essence of the rule of law that ordinary citizens can know what the rules are? Can be confident that the rules are not guidelines but, you know, The Law?

We live in a country where the law has not only become unintelligible, written in thousand-page chunks, but has morphed into a giant mass of silly putty that can be reshaped as our rulers find convenient.

via The Obamacare employer mandate delay is another symptom of our crumbling ‘rule of law’ | AEIdeas.

If Business Is Evil For Cutting Hours Due To Obamacare, What About Colleges?

When the Affordable Care Act passed in early 2010, many in academia—faculty and students alike—cheered on. But now that its provisions are going into effect, some of these same people are learning firsthand that Obamacare has some nasty side effects.

A new piece in the Wall Street Journal reports that many colleges are cutting back on the number of hours worked by adjunct professors, in order to avoid new requirements that they provide healthcare to anyone working over 30 hours per week. This is terrible news for a lot of people; 70 percent of professors work as adjuncts and many will now have to cope with a major pay cut just as requirements that they buy their own health insurance go into effect.

via Universities Bludgeon Adjuncts With Obamacare Loophole | Via Meadia.

Taubes: Nutrition and Obesity “Research” Is Generally *Not* Science

[E]very time in the past that these researchers had claimed that an association observed in their observational trials was a causal relationship, and that causal relationship had then been tested in experiment, the experiment had failed to confirm the causal interpretation — i.e., the folks from Harvard got it wrong. Not most times, but every time. No exception. Their batting average circa 2007, at least, was .000.

I never used the word scientist to describe the people doing nutrition and obesity research, except in very rare and specific cases. Simply put, I don’t believe these people do science as it needs to be done; it would not be recognized as science by scientists in any functioning discipline.

Science is ultimately about establishing cause and effect. It’s not about guessing. You come up with a hypothesis — force x causes observation y — and then you do your best to prove that it’s wrong. If you can’t, you tentatively accept the possibility that your hypothesis was right. Peter Medawar, the Nobel Laureate immunologist, described this proving-it’s-wrong step as the ”the critical or rectifying episode in scientific reasoning.” Here’s Karl Popper saying the same thing: “The method of science is the method of bold conjectures and ingenious and severe attempts to refute them.” The bold conjectures, the hypotheses, making the observations that lead to your conjectures… that’s the easy part. The critical or rectifying episode, which is to say, the ingenious and severe attempts to refute your conjectures, is the hard part. Anyone can make a bold conjecture. (Here’s one: space aliens cause heart disease.) Making the observations and crafting them into a hypothesis is easy. Testing them ingeniously and severely to see if they’re right is the rest of the job — say 99 percent of the job of doing science, of being a scientist.

[B]ecause this is supposed to be a science, we ask the question whether we can imagine other less newsworthy explanations for the association we’ve observed. What else might cause it? An association by itself contains no causal information. There are an infinite number of associations that are not causally related for every association that is, so the fact of the association itself doesn’t tell us much.

[A]s we move from the bottom quintile of meat-eaters (those who are effectively vegetarians) to the top quintile of meat-eaters we see an increase in virtually every accepted unhealthy behavior — smoking goes up, drinking goes up, sedentary behavior (or lack of physical activity) goes up — and we also see an increase in markers for unhealthy behaviors — BMI goes up, blood pressure, etc. So what could be happening here?

[P]eople who comply with their doctors’ orders when given a prescription are different and healthier than people who don’t. This difference may be ultimately unquantifiable. The compliance effect is another plausible explanation for many of the beneficial associations that epidemiologists commonly report, which means this alone is a reason to wonder if much of what we hear about what constitutes a healthful diet and lifestyle is misconceived.

[W]henever epidemiologists compare people who faithfully engage in some activity with those who don’t — whether taking prescription pills or vitamins or exercising regularly or eating what they consider a healthful diet — the researchers need to account for this compliance effect or they will most likely infer the wrong answer. They’ll conclude that this behavior, whatever it is, prevents disease and saves lives, when all they’re really doing is comparing two different types of people who are, in effect, incomparable.

[O]bservational studies may have inadvertently focused their attention specifically on, as Jerry Avorn says, the “Girl Scouts in the group, the compliant ongoing users, who are probably doing a lot of other preventive things as well.”

It’s this compliance effect that makes these observational studies the equivalent of conventional wisdom-confirmation machines.

So when we compare people who ate a lot of meat and processed meat in this period to those who were effectively vegetarians, we’re comparing people who are inherently incomparable. We’re comparing health conscious compliers to non-compliers; people who cared about their health and had the income and energy to do something about it and people who didn’t. And the compliers will almost always appear to be healthier in these cohorts because of the compliance effect if nothing else. No amount of “correcting” for BMI and blood pressure, smoking status, etc. can correct for this compliance effect, which is the product of all these health conscious behaviors that can’t be measured, or just haven’t been measured. And we know this because they’re even present in randomized controlled trials. When the Harvard people insist they can “correct” for this, or that it’s not a factor, they’re fooling themselves. And we know they’re fooling themselves because the experimental trials keep confirming that.

This is why the best epidemiologists — the one’s I quote in the NYT Magazine article — think this nutritional epidemiology business is a pseudoscience at best. Observational studies like the Nurses’ Health Study can come up with the right hypothesis of causality about as often as a stopped clock gives you the right time. It’s bound to happen on occasion, but there’s no way to tell when that is without doing experiments to test all your competing hypotheses. And what makes this all so frustrating is that the Harvard people don’t see the need to look for alternative explanations of the data — for all the possible confounders — and to test them rigorously, which means they don’t actually see the need to do real science.

Now we’re back to doing experiments — i.e., how we ultimately settle this difference of opinion. This is science. Do the experiments.

So we do a randomized-controlled trial. Take as many people as we can afford, randomize them into two groups — one that eats a lot of red meat and bacon, one that eats a lot of vegetables and whole grains and pulses-and very little red meat and bacon — and see what happens. These experiments have effectively been done. They’re the trials that compare Atkins-like diets to other more conventional weight loss diets — AHA Step 1 diets, Mediterranean diets, Zone diets, Ornish diets, etc. These conventional weight loss diets tend to restrict meat consumption to different extents because they restrict fat and/or saturated fat consumption and meat has a lot of fat and saturated fat in it. Ornish’s diet is the extreme example. And when these experiments have been done, the meat-rich, bacon-rich Atkins diet almost invariably comes out ahead, not just in weight loss but also in heart disease and diabetes risk factors. I discuss this in detail in chapter 18 of Why We Get Fat, ”The Nature of a Healthy Diet.” The Stanford A TO Z Study is a good example of these experiments. Over the course of the experiment — two years in this case — the subjects randomized to the Atkins-like meat- and bacon-heavy diet were healthier. That’s what we want to know.

Now Willett and his colleagues at Harvard would challenge this by saying somewhere along the line, as we go from two years out to decades, this health benefit must turn into a health detriment. How else can they explain why their associations are the opposite of what the experimental trials conclude? And if they don’t explain this away somehow, they might have to acknowledge that they’ve been doing pseudoscience for their entire careers. And maybe they’re right, but I certainly wouldn’t bet my life on it.

Ultimately we’re left with a decision about what we’re going to believe: the observations, or the experiments designed to test those observations. Good scientists will always tell you to believe the experiments. That’s why they do them.

Conventional methods assume all errors are random and that any modeling assumptions (such as homogeneity) are correct. With these assumptions, all uncertainty about the impact of errors on estimates is subsumed within conventional standard deviations for the estimates (standard errors), such as those given in earlier chapters (which assume no measurement error), and any discrepancy between an observed association and the target effect may be attributed to chance alone. When the assumptions are incorrect, however, the logical foundation for conventional statistical methods is absent, and those methods may yield highly misleading inferences.

Systematic errors can be and often are larger than random errors, and failure to appreciate their impact is potentially disastrous.

Via Science, Pseudoscience, Nutritional Epidemiology, and Meat.

Hugo Chavez Hit By Cuba’s Surgical Strike

Venezuela’s Hugo Chavez is dying of cancer in Havana, in a live demonstration of Cuba’s vaunted socialized medical care. He went there instead of Brazil because he wanted to make a political statement. What irony.

As party cronies hover at his bedside, Cuban officials bark orders to the government in Caracas, and red-shirted Chavistas hold vigils, all signs are pointing to an imminent exit for the Venezuelan leader who controls a huge part of the world’s oil.

He’s going out exactly as he wouldn’t have liked — helpless and at the mercy of doctors, a far cry from the blaze of heroic socialist glory he might have preferred.

Most galling for him: It didn’t have to happen this way.

His expected demise will be entirely due to his gullibility to leftist propaganda and bad choices that came of it.

via Venezuela’s Hugo Chavez Sinks On Credulity In Cuban Health Care – Investors.com.