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Archive for July 2018

The Worst Drug Crisis in American History

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In the NY Times Jessica Bruder reviews a timely nonfiction book:

Dealers, Doctors, and the Drug Company That Addicted America
By Beth Macy
Illustrated. 376 pp. Little, Brown & Company. $28.

In 2000, a doctor in the tiny town of St. Charles, Va., began writing alarmed letters to Purdue Pharma, the manufacturer of OxyContin. The drug had come to market four years earlier and Art Van Zee had watched it ravage the state’s poorest county, where he’d practiced medicine for nearly a quarter-century. Older patients were showing up at his office with abscesses from injecting crushed-up pills. Nearly a quarter of the juniors at a local high school had reported trying the drug. Late one night, Van Zee was summoned to the hospital where a teenage girl he knew — he could still remember immunizing her as an infant — had arrived in the throes of an overdose.

Van Zee begged Purdue to investigate what was happening in Lee County and elsewhere. People were starting to die. “My fear is that these are sentinel areas, just as San Francisco and New York were in the early years of H.I.V.,” he wrote.

Since then, the worst drug crisis in America’s history — sparked by OxyContin and later broadening into heroin and fentanyl — has claimed hundreds of thousands of lives, with no signs of abating. Just this spring, public health officials announced a record: The opioid epidemic had killed 45,000 people in the 12-month span that ended in September, making it almost as lethal as the AIDS crisis at its peak.

Van Zee’s prophecy and other early warnings haunt the pages of “Dopesick: Dealers, Doctors, and the Drug Company That Addicted America,” a harrowing, deeply compassionate dispatch from the heart of a national emergency. The third book by Beth Macy — the author, previously, of “Factory Man” and “Truevine” — is a masterwork of narrative journalism, interlacing stories of communities in crisis with dark histories of corporate greed and regulatory indifference.

Macy began investigating the drug epidemic in 2012, as it seeped into the suburbs around her adopted hometown, Roanoke, Va., where she worked for 20 years as a reporter at The Roanoke Times. From there, she set out to map the local onto the national. “If I could retrace the epidemic as it shape-shifted across the spine of the Appalachians, roughly paralleling I-81 as it fanned out from the coalfields and crept north up the Shenandoah Valley, I could understand how prescription pill and heroin abuse was allowed to fester, moving quietly and stealthily across this country, cloaked in stigma and shame,” she writes.

[Read Beth Macy on the life and death of an addict]

The word “allowed” is a quiet curse. The further Macy wades into the wreckage of addiction, the more damning her indictment becomes. The opioid epidemic didn’t have to happen. It was a human-made disaster, predictable and tremendously lucrative. At every stage, powerful figures permitted its progress, waving off warnings from people like Van Zee, participating in what would become, in essence, a for-profit slaughter. Or as Macy puts it: “From a distance of almost two decades, it was easier now to see that we had invited into our country our own demise.”

Particularly grotesque is the enthusiasm with which Purdue peddled its pills. In the first five years OxyContin was on the market, total bonuses for the company’s sales staff grew from $1 million to $40 million. Zealous reps could earn quarterly bonuses as high as $100,000, one former salesperson told Macy, adding, “It behooved them to have the pill mills writing high doses.”

Doctors were plied with all-expense-paid resort trips, free tanks of gas and deliveries of Christmas trees and Thanksgiving turkeys. There were even “starter coupons” offering new patients a free 30-day supply. As sales rocketed into the billions, noxious side effects began to emerge. Chief among them was the creation of a legion of addicts who, desperate to stave off withdrawal, made the leap to cheap heroin and, later, fentanyl. (“Four out of five heroin addicts come to the drugs … through prescribed opioids,” Macy notes pointedly.) . . .

Continue reading.

Again we see that a corporation has no ethical or moral (or legal) limits when it comes to profit. Profit seems to justify anything. That’s very destructive, as we see (opioid crisis, Superfund sites, public parks sold off, endanger species to become extinct, and on and on).

Written by LeisureGuy

31 July 2018 at 7:09 pm

Great dinner: Chicken hearts and veggies

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You don’t have the use the veggies I used.Just use some allium (I used garlic and long green onion and scallions, but regular onions would work, as would leeks or shallots. For the yu choy sum and Shanghai bok choy, just use other leafy vegetables: red chard, regular bok choy, kale (red or green). Use a plain zucchini, or summer squash if you can get it. It’s your food: cook to suite your taste. But this really tasted good to me.

I used my large 4-qt All-Clad sauté pan.

1 Tbsp duck fat (or extra-virgin olive oil—I had duck fat, so I used it)

When fat is hot, add

1 lb chicken hearts
salt and pepper

Sauté chicken hearts for a few minutes. The rest of the dish will in effect be steamed, so if you want the hearts to be browned, now’s the time to do it.

Yu choy sum, about 6 little bunches, chopped
3 Baby Shanghai Bok choy, chopped
1 yellow zucchini, quartered lengthwise and chopped
2 long green onions, chopped (this is a Chinese vegetable)
1 bunch scallions, chopped
10-12 stalks thinnish asparagus, chopped
10 San Marzano cherry tomatoes, sliced
about 1.5 cups oyster mushrooms, caps and stems, chopped
1 jalapeño pepper, chopped small (including core and ribs)
2 cloves of the giant garlic, minced
salt, Aji-no-moto, fair amount of black pepper

I had never even heard of yu choy sum, but it looked very fresh and nice, and hey! it’s greens. I know how to cook greens. And it turns out to be quite yummy.

I cooked that 15 minutes with top on, 15 with top off. I served with topped with pickled red onions from my butcher (where I got the duck fat, in fact).

Really extremely tasty.

Written by LeisureGuy

31 July 2018 at 5:32 pm

Posted in Food, Low carb, Recipes

Major Quantum Computing Advance Made Obsolete by Teenager

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Bob and Ray had a routine with Wally Ballou interviewing a washed-up has-been of a baseball umpire. One great call: an enrage batter throws his bat into the air, and the Ray character of the umpire says, “If that bat comes down, you’re out of the game.” (Great line, IMO)

His lament in the interview, “Somewhere this game just passed me by.” I had sort of that feeling on reading this Quanta article by Kevin Hartnett:

A teenager from Texas has taken quantum computing down a notch. In a paper posted online earlier this month, 18-year-old Ewin Tang proved that ordinary computers can solve an important computing problem with performance potentially comparable to that of a quantum computer.

In its most practical form, the “recommendation problem” relates to how services like Amazon and Netflix determine which products you might like to try. Computer scientists had considered it to be one of the best examples of a problem that’s exponentially faster to solve on quantum computers — making it an important validation of the power of these futuristic machines. Now Tang has stripped that validation away.

“This was one of the most definitive examples of a quantum speedup, and it’s no longer there,” said Tang, who graduated from the University of Texas, Austin, in spring and will begin a Ph.D. at the University of Washington in the fall.

In 2014, at age 14 and after skipping the fourth through sixth grades, Tang enrolled at UT Austin and majored in mathematics and computer science. In the spring of 2017 Tang took a class on quantum information taught by Scott Aaronson, a prominent researcher in quantum computing. Aaronson recognized Tang as an unusually talented student and offered himself as adviser on an independent research project. Aaronson gave Tang a handful of problems to choose from, including the recommendation problem. Tang chose it somewhat reluctantly.

“I was hesitant because it seemed like a hard problem when I looked at it, but it was the easiest of the problems he gave me,” Tang said.

The recommendation problem is designed to give a recommendation for products that users will like. Consider the case of Netflix. It knows what films you’ve watched. It knows what all of its other millions of users have watched. Given this information, what are you likely to want to watch next?

You can think of this data as being arranged in a giant grid, or matrix, with movies listed across the top, users listed down the side, and values at points in the grid quantifying whether, or to what extent, each user likes each film. A good algorithm would generate recommendations by quickly and accurately recognizing similarities between movies and users and filling in the blanks in the matrix.

In 2016 the computer scientists Iordanis Kerenidis and Anupam Prakash published a quantum algorithm that solved the recommendation problem exponentially faster than any known classical algorithm. They achieved this quantum speedup in part by simplifying the problem: Instead of filling out the entire matrix and identifying the single best product to recommend, they developed a way of sorting users into a small number of categories — do they like blockbusters or indie films? — and sampling the existing data in order to generate a recommendation that was simply good enough.

At the time of Kerenidis and Prakash’s work, there were only a few examples of problems that quantum computers seemed to be able to solve exponentially faster than classical computers. Most of those examples were specialized — they were narrow problems designed to play to the strengths of quantum computers (these include the “forrelation” problem Quanta covered earlier this year). Kerenidis and Prakash’s result was exciting because it provided a real-world problem people cared about where quantum computers outperformed classical ones.

“To my sense it was one of the first examples in machine learning and big data where we showed quantum computers can do something that we still don’t know how to do classically,” said Kerenidis, a computer scientist at the Research Institute on the Foundations of Computer Science in Paris.

Kerenidis and Prakash proved that a quantum computer could solve the recommendation problem exponentially faster than any known algorithm, but they didn’t prove that a fast classical algorithm couldn’t exist. So when Aaronson began working with Tang in 2017, that was the question he posed — prove there is no fast classical recommendation algorithm, and thereby confirm Kerenidis and Prakash’s quantum speedup is real.

“That seemed to me like an important ‘t’ to cross to complete this story,” said Aaronson, who believed at the time that no fast classical algorithm existed.

Tang set to work in the fall of 2017, intending for the recommendation problem to serve as a senior thesis. For several months Tang struggled to prove that a fast classical algorithm was impossible. As time went on, Tang started to think that maybe such an algorithm was possible after all.

“I started believing there is a fast classical algorithm, but I couldn’t really prove it to myself because Scott seemed to think there wasn’t one, and he was the authority,” Tang said.

Finally, with the senior thesis deadline bearing down, Tang wrote to Aaronson and admitted a growing suspicion: “Tang wrote to me saying, actually, ‘I think there is a fast classical algorithm,’” Aaronson said.

Throughout the spring Tang wrote . . .

Continue reading.

Written by LeisureGuy

31 July 2018 at 4:39 pm

Posted in Math, Science, Software

Workers, systematically ripped off, need to take back the government

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Read these articles:

Eric Levitz in New YorkNew Study Confirms That American Workers Are Getting Ripped Off The graph above is from that article, which also has this chart:

And also read Emily Stewart’s report in VoxOne chart that shows how much worse income inequality is in America than Europe,

Written by LeisureGuy

31 July 2018 at 3:14 pm

Trump Administration Ordered to Stop Injecting Migrant Kids With Psychotropic Drugs Without Consent

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Amanda Arnold reports at

After reading testimony from several migrant children who said they’d been injected with psychotropic medication without their consent, a federal judge ruled that the Texas facility holding them had violated state child welfare laws.

Per the Washington Post, U.S. District Judge Dolly Gee of Los Angeles ordered the Trump administration to either obtain consent or a court order before giving medication to minors, unless there is a dire emergency. She also demanded the immediate evacuation of all children — except those deemed to pose a “risk of harm” — from the offending facility, Shiloh Residential Treatment Center in Manvel, Texas, which is contracted by the U.S. Office of Refugee Resettlement.

Staff members at the facility admitted to violating welfare laws by signing off on medications for children — but the government was quick to defend this practice, claiming that drugs were only given to children “on an emergency basis,” when their psychiatric symptoms became “extreme.” The judge took issue with this explanation, noting that some children said they’d been given meds “every morning and every night.”

One girl held at Shiloh, who testified as Isabella M. in court filings, said she was prescribed multiple psychotropic medications while she was detained in the center. She also recalled watching staff members “forcefully give [other children] medication four times.”

“Two staff members pinned down the girl … and a doctor gave her one or two injections,” she said. According to the child’s testimonies, while some minors were given injections, more were given pills on a daily basis. She said the staff members and doctors claimed these drugs were vitamins, but they caused side effects like nausea, dizziness, depression, and weight gain.

According to Isabella’s mother, . . .

Continue reading.

Written by LeisureGuy

31 July 2018 at 1:21 pm

Why the Most Important Idea in Behavioral Decision-Making, Loss Aversion, Is a Fallacy

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David Gal, professor of marketing at the University of Illinois at Chicago, writes in Scientific American:

Loss aversion, the idea that losses are more psychologically impactful than gains, is widely considered the most important idea of behavioral decision-making and its sister field of behavioral economics. To illustrate the importance loss aversion is accorded, Daniel Kahneman, winner of the 2002 Nobel Prize in economics, wrote in his 2011 best-selling book, Thinking Fast and Slow, that “the concept of loss aversion is certainly the most significant contribution of psychology to behavioral economics.” As another illustration, when Richard Thaler was awarded the 2017 Nobel Prize in economics, the phrase “loss aversion” appeared 24 times in the Nobel Committee’s description of his contributions to science.

Why has such profound importance been attributed to loss aversion? Largely, it is because it is thought to reflect a fundamental truth about human beings—that we are more motivated by our fears than by our aspirations. This conclusion, it is thought, has implications for almost every aspect of how we live our lives.

However, as documented in a recent critical review of loss aversion by Derek Rucker of Northwestern University and myself, published in the Journal of Consumer Psychology, loss aversion is essentially a fallacy. That is, there is no general cognitive bias that leads people to avoid losses more vigorously than to pursue gains. Contrary to claims based on loss aversion, price increases (ie, losses for consumers) do not impact consumer behavior more than price decreases (ie, gains for consumers). Messages that frame an appeal in terms of a loss (eg, “you will lose out by not buying our product”) are no more persuasive than messages that frame an appeal in terms of a gain (eg, “you will gain by buying our product”).

People do not rate the pain of losing $10 to be more intense than the pleasure of gaining $10. People do not report their favorite sports team losing a game will be more impactful than their favorite sports team winning a game. And people are not particularly likely to sell a stock they believe has even odds of going up or down in price (in fact, in one study I performed, over 80 percent of participants said they would hold on to it).

To be sure it is true that big financial losses can be more impactful than big financial gains, but this is not a cognitive bias that requires a loss aversion explanation, but perfectly rational behavior. If losing $10,000 means giving up the roof over your head whereas gaining $10,000 means going on an extra vacation, it is perfectly rational to be more concerned with the loss than the gain. Likewise, there are other situations where losses are more consequential than gains, but these require specific explanations not blanket statements about a loss aversion bias.

If what I am claiming is true, why has belief in loss aversion persisted so strongly? An idealized view of science is that theories are accepted or rejected based solely on empirical evidence. In fact, science is not simply an objective search for truth, but also a social process, in which proponents of a theory must convince other scientists, through logic and argumentation, of how evidence should be interpreted.

However, this process advantages incumbent theories over challengers for a number of reasons, including confirmation bias, social proof, ideological complacency, and the vested interests of scientists whose reputations and even sense of self are tied to existing theories. A consequence is scientific inertia, where weak or ill-founded theories take on a life of their own, sometimes even gaining momentum despite evidence that puts their veracity in doubt.

In the case of loss aversion, contradictory evidence has tended to be dismissed, ignored or explained away, while ambiguous evidence has tended to be interpreted in line with loss aversion. For example, a paper purporting to illustrate that price increases are more impactful than price decreases received 65 citations in Google Scholar in 2016, whereas a follow-up paper challenging this view received only 17 citations.

Moreover, belief in loss aversion has meant that phenomena that have nothing to do with loss aversion have nonetheless been interpreted to reflect loss aversion. For example, the sunk cost effect, the finding that people are more likely to continue an endeavor once an investment in it has been made, has been attributed to loss aversion. While the sunk cost effect might reflect a reluctance to recognize losses, this is not relevant to loss aversion, which requires a comparison be made between losses and gains.

In sum, . . .

Continue reading.

Written by LeisureGuy

31 July 2018 at 12:38 pm

Conservative Think Tank Says Medicare For All Would Save $2 Trillion

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Good news about Medicare for All, though probably viewed as bad news by the think tank involved, though that would be perverse. Kevin Drum notes:

Here’s some good news. The libertarians at the Mercatus Center did a cost breakdown of Bernie Sanders’ Medicare for All plan and concluded that it would save $2 trillion during its first ten years:

Now, as you might guess, this was not the spin the Mercatus folks put on their study. Their headline is “M4A Would Place Unprecedented Strain on the Federal Budget.” This isn’t really true, of course, since M4A would absorb all the costs of our current health care system but would also absorb all the payments we make to support it. That includes current taxes (for Medicare, Medicaid, and Obamacare), premiums paid by employers, premiums paid by individuals, and out-of-pocket costs from individuals. Instead of going straight to doctors, hospitals, and insurance companies, it would go instead to the federal government, which would then pay everyone else. It’s a lot of money, but it’s no particular “strain” on anything.

And overall we’d save at least $2 trillion over ten years. Blahous thinks the number would be be less because lots of people would flock to use free health care that they hadn’t used before, but most health economists disagree. Demand for health care would probably stay about the same, while costs would be more strongly contained because everything would be paid for out of tax dollars—and voters are strongly motivated to keep taxes low.

Update: See also this report in The Hill.

Written by LeisureGuy

31 July 2018 at 10:50 am

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