Tag Archives: Medicine

First Cryonics Hour

Me two years ago:

I hereby offer to talk for one hour on any subject to anyone who can show me they’ve newly signed up for cryonics. You can record the conversation, publish it, and can sell your time to someone else.

Stuart Armstrong has signed up for cryonics, and then redeemed my offer. Congrats Stuart! We talked for an hour, and he recorded the conversation. If he does something with that recording, I’ll post a link here.

Any other takers?

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Promises Signal

Promises can both 1) help others predict and rely on our future behavior, and 2) signal our current feelings to others. The signaling function seems to dominate:

People who had the most positive relationship feelings and who were most motivated to be responsive to the partner’s needs made bigger promises than did other people but were not any better at keeping them. Instead, promisers’ self-regulation skills, such as trait conscientiousness, predicted the extent to which promises were kept or broken. … Participants who were [caused to] focused on their feelings for their partner promised more, whereas participants who generated a plan of self-regulation followed through more on their promises. …

When people make promises to address a point of contention with their partner, they seem to get swept up in what they want to do for their partner. A promise situation might appear as an opportunity to be responsive to a partner’s needs and demonstrate the loving feelings they experience for the other person, and it is these feelings they are thinking of when they make promises. … If promised behaviors can be completed immediately after promising rather than be sustained over a longer period of time, then the link between positive relationship feelings and extent of followthrough reemerges. (more)

If relationship promises mainly function to signal our current feelings, that makes it more plausible that paying and pushing for medicine for our associates serves a similar function of showing that we care. We humans apparently do relatively little checking later of whether promises were kept, or if medicine helped.

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Let Brits Do US Med Eval

Here is my entire 300 word NYT oped:

Medicare should stop paying for treatments that the British Medical Journal says probably don’t work.

Today, United States agencies that try to not pay for ineffective treatments face the wrath of Congress, egged on by the surgeons and drug companies whose revenue is threatened. So far, U.S. agencies have pretty much always backed down, and just paid for everything.

The United Kingdom, where, on average, people live longer than in the U.S., spends only about 9 percent of gross domestic product on medicine, compared with our 18 percent. The British control costs in part by having the will to empower a hard-nosed agency, the National Institute for Health and Clinical Experience (N.I.C.E.), to study treatments and declare some ineffective. Some hope the United States will create a similar agency, but I fear it would be hopelessly politicized and declawed.

My solution: admit we are cost-control wimps, and outsource our treatment evaluation to the U.K. Pass a simple law saying Medicare (and Medicaid) won’t cover treatments considered but not positively appraised by the Britain’s national health institute.

Even better, use clinical evidence evaluations of the British Medical Journal. They’ve classified more than 3,000 treatments as either unknown effectiveness (51 percent), beneficial (11 percent), likely to be beneficial (23 percent), trade-off between benefits and harms (7 percent), unlikely to be beneficial (5 percent) and likely to be ineffective or harmful (3 percent). Let’s at least stop paying for these last two categories of treatments! And to put pressure on doctors to collect evidence, let’s stop paying for “unknown effectiveness” treatments after 10 years of use.

Yes, eventually, this evaluation source would become corrupted, as were the asset risk rating agencies that contributed to the recent financial meltdown. But we’d at least have a few more years to come up with a better solution.

Interestingly, two of the nine other opeds on “What Medicare Services to Cut, Now” wanted more hospice care.

Added 4June: Will Wilkinson:

This reminds me of another proposal, similar in spirit, to de-nationalise the drug-approval process … [by] Daniel Klein …:

One idea is to recognize the drug approvals of, say, 15 other governments. That is, we reform the U.S. system so that if the drug-approval agency of even one of those 15 countries approves a drug for that country, then the drug is automatically approved in the United States.

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Blocked State Innovation

I’ve complained that regulation usually slows innovation. For example, huge driverless cars gains seem needlessly delayed by excess regulation (Tyler agrees). The problem, however, is not government per se, but the citizens to whom government defers. Politics is not about policy; voters are far more interested in showing off symbolic stances than in giving citizens more of what they want.

But to be fair, citizens hinder not only private innovation, but also government innovation. Long ago when people were imagining a future of cheap computing and communication, they imagined dramatic gains from government databases and citizen monitoring. But then some warned of how such data and monitoring could support tyranny, and ever since most voters have been so eager to signal their disapproval of such Big Brother domination that they are unwilling to consider the most promising government innovations in data and monitoring.

For example, me a year ago:

Overall my students oppose change, moderately favoring whatever is the status quo. So I was quite surprised to see … 85% of my students said yes to: Should all medical practice data be published, aside from data identifying patients?

I assigned this paper topic again this year and, combining the two years, 76% of 76 students favored the change, which correlated 0.29 with student ability to identify relevant pro/con arguments. Again, I don’t grade students on their position, I don’t say what I support, and students usually oppose change. (For example, they overwhelmingly opposed stricter public-place policies on hand washing after sneezing or using restrooms.)

Last year, commenters’ main complaint was that it is impossible guarantee privacy. And this is true. In principle, any piece of info you publish about someone could be the last little clue someone else needs to uncover a great secret about them. It all depends on what other info people reveal, and to whom. The only safe policy is to never publish anything about anyone. And since info supposedly only visible to government employees are often leaked via bribes, the only really safe policy is to never collect any info.

But note that this same argument applies to every piece of info the government reveals about anyone, including date of birth, addresses, who/when they marry or divorce, professional licenses, lawsuits, bankruptcies, tax liens, criminal records, etc. The reason few complain about privacy leaks due to such revelations is that most folks have adapted their other info behavior to expecting this info to be made public.

Similarly, if we gave sufficient advanced warning on a new regime of revealing all med info (minus directly identifying info), most people could adapt their other info behavior to preserve the privacy they want. Don’t let friends drive you to the doc if you don’t want them to know who is your doc. Of course some would mistakenly reveal themselves, and illegal bribery would reveal more. But that can be a price worth paying if there is much to be gained.

Alas, while even my undergrads can see that revealing all med info could easily meet a cost benefit test, voter distaste for anything smacking of Big Brother will probably long block this innovation. This even though recent legal changes go a long way to actually enabling future dictators:

Our Presidents can now, on their own: order assassinations, including American citizens; operate secret military tribunals; engage in torture; enforce indefinite imprisonment without due process; order searches and seizures without proper warrants. (more)

Citizens don’t make a careful tradeoff between social value and preventing future dictators. Instead, thoughtless voters enable Big Brother while symbolically opposing him, and block useful government innovation in the process.

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School Death Puzzle

When Ken Lee looked at 367,101 folks followed over 11 years, he found (in Table 12) that richer folks consistently died less. But for education, the trend wasn’t as consistent:


Folks who graduate from high school die less than those who only start high school, those who graduate from college die even less, and those who have some grad school live even more. But, folks with no school at all do as well as college graduates! And compared with those who stop at three years of high school, those who get less school than three years of high school seem to suffer no health penalty – if anything they die less!  This fits with seeing higher mortality in states with more high school graduates, after controlling for college grads, but is odd. What gives?

Could this be a status effect, where those who didn’t buy into school as an ideal don’t mind that they didn’t get so much school?

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Death Cause Correlates

Over the years I’ve seen many studies correlating overall death rates with other features, and also seen studies on correlates of particular causes of death, but until Ken Lee’s thesis I’d never seen how death correlates change with broad categories of death causes. Yesterday I pointed to one disturbing correlate: more med spending correlates with more cancer deaths, but not with more deaths from other causes.

That data also found injury deaths increasing more with alcohol use, which makes sense. While no population density estimates were significant, density’s most positive correlation with death was for “other” deaths, which contains most known contagious conditions. This also makes sense, as density increases contagion.

That was all from Lee’s chapter 2, where he looks at 50 states over 28 years. In chapter 3 Lee turns to a much larger data set, 367,101 adults from the National Longitudinal Mortality Study, followed over 11 years during which 9.1% of them died. Here are a few selections from Lee’s Table 14, where he breaks down deaths into cancer, heart attack, injury, and other:


If docs are especially bad at treating cancer, then we should expect those who use docs more to do worse at cancer. And in fact women, the rich, and the well educated do worse at cancer. Since there are many more dangerous objects in rural and poor lives, it also makes sense that such folks suffer injury deaths more.

If the main reason rural folks die less is that lower density reduces contagion, we’d expect the rural effect to be largest for “other” deaths, and that is what we find. Interestingly, that is also the kind of death which marriage best prevents – does married life prevent contagion compared with single life?

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Skip Cancer Screens

Ken Lee’s result that high med spending states tend to have more cancer deaths inspired me to look up the med lit on cancer screening.  I turned to Cochrane Reviews, high quality med lit reviews.  Here are the reviews I found on cancer screening:

Breast cancer:

Eight eligible trials were identified. We excluded a biased trial and included 600,000 women in the analyses. Three trials with adequate randomisation [with 260,000 women] did not show a significant reduction in breast cancer mortality at 13 years; four trials with suboptimal randomisation showed a significant reduction in breast cancer mortality with an RR [risk ratio] of 0.75 (95% CI 0.67 to 0.83). … Significantly more breast operations (mastectomies plus lumpectomies) were performed in the study groups than in the control groups: RR 1.31 (95% CI 1.22 to 1.42) for the two adequately randomised trials. … Breast cancer mortality was an unreliable outcome that was biased in favour of screening, mainly because of differential misclassification of cause of death. The trials with adequate randomisation did not find an effect of screening on cancer mortality, including breast cancer, after 10 years (RR 1.02, 95% CI 0.95 to 1.10) or on all-cause mortality after 13 years.

Colorectal cancer:

Four RCTs [randomized controlled trials] … involved 327,043 participants in Denmark, Sweden, the United States, and the United Kingdom. … Combining the four RCTs show that screening results in a statistically significant relative reduction in CRC mortality of 16% (fixed and random effects models: RR 0.84, 95% confidence interval [CI] 0.78–0.90) … Combining the four RCTs did not show any significant difference in all-cause mortality between the screening and control groups.

Prostate cancer:

Five RCTs with a total of 341,351 participants were included in this review. … The methodological quality of three of the studies was assessed as posing a high risk of bias. Our analysis of the five studies showed no statistically significant reduction in prostate cancer-specific or all-cause mortality among the whole population of men randomised to screening versus controls.

Lung cancer:

We included seven trials (six randomised controlled studies and one non-randomised controlled trial) with a total of 245,610 subjects. There were no studies with an unscreened control group. Frequent screening with chest x-rays was associated with an 11% relative increase in mortality from lung cancer compared with less frequent screening (RR 1.11, 95% CI 1.00 to 1.23).

Wow.  While cancer screening does consistently lead to more cancer detection and more cancer treatment, it consistently doesn’t lead to lower mortality.

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Beware Cancer Med

Chapter 2 of Ken Lee’s thesis compares med spending and age-adjusted deaths across the 50 US states from 1980 to 2007. Lee’s baseline model finds that deaths increases with smoking use, alcohol use, population density, and med spending: a 10% increase in med spending increases deaths by 0.85%. Breaking down this med spending death effect by drug vs. non-drug spending, and by four causes of death (cancer, heart attack, injury, and other), Lee finds (in Tables 5,6) that med spending hurts mainly because increasing non-drug med spending by 10% increases cancer deaths by 2.1%:

Cause of Death, Drug vs Non-Drug Med Spending

The apparent lesson: avoid cancer docs, and especially their non-drug cancer treatments. It seems some places tend to spend more on med overall, and when they spend more on cancer patients, those patients die no less, and maybe more. That fits with cancer patients living longer when they go to hospice and get no cancer treatment and with randomized trials of cancer screening consistently showing no effect on total mortality. Other explanations, however, are that high med spending places tend to classify more deaths as due to cancer, or that med treatment of all sorts tends to cause cancer.

For you stat whizzes, Lee uses state and year fixed effects, and uses per capita physicians, beds, and dental spending as med spending instruments to disentangle the direction of causation.  He picked that instrument set because it had the smallest bootstrap variance, and passed many tests. Here is Lee’s baseline model (from Table 3):

Continue reading "Beware Cancer Med" »

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How US States Vary

Ken Lee just recieved his Ph.D. in economics from GMU; I was his thesis advisor; his thesis is here. I am impressed enough with Ken’s thesis that I’ll take the next few posts to describe some of his main findings.  The first finding I’ll describe: The main way that US states vary is in their health.

Ken collected 81 features of states, 56 cultural rankings and 25 demographic variables (listed below), and did a factor an analysis on them.  A factor analysis finds a few linear combinations of features that can explain the most variance in whole set of features; the variation of all the features could result from variation in just a few behind-the-scenes factors, plus error.

The biggest factor, explaining 27% of the variance between US states, was health – some states are just healthier than others, and this fact can explain many other things about those states.  Here are the three biggest factors:

  1. (27% of variance): Top five features: “low cancer deaths, low cardiovascular deaths, low smoking rates, low levels of unnecessary medical care, low obesity rates,” Also: “high well-being index, high exercise rates, healthiest, low mortality rates for blacks and whites, higher in education (IQ Rank, Percentage of Graduates, and Smartest), higher in health (Healthiest, Exercise Frequency, and Percentage with No Insurance), and lower in crime rates (Crime Rate and Violent Crime Rate) rankings.” Map: Factor 1
  2. (15% of variance): Top five features: “low occupational death rates, high in women’s rights, high in primary care physicians per capita, high in amount of fruit eaten per capita, low in percentage on poverty.” Also: “low in teen births, high on $ spent on K-12 education, high $ for teacher salaries, smartest … a higher percentage of people in the 25-44 age group, higher income, high college graduation rate, and higher urbanization.” Map:
    Factor 2
  3. (14% of variance): Top five features: “low rates of infections (HIV, STD), high in IQ, low overall crime rates, high in graduates, low in those having no health insurance.” Also: “low in violent crime, healthiest, low in percentage urban … regular church attendance, a high regard for religion, worse overall state economic health, high manufacturing employment, and high farming output.” Map: Factor 3

To me, factor 1 seems mainly about health, factor 2 seems about left (~forager) idealism  — fruit, women’s rights, safety rules, helping the poor, and spending lots on docs and teachers — and factor 3 seems about right (~farmer) idealism — rural, religious, low crime, sexual restraint, make real stuff, finish what you start.

The fact that health is the biggest factor says that health is very important, even beyond its direct benefits. And the fact that health and a tendency to spend on docs are largely independent says that medicine isn’t very important for health, and there should be enough variation among states to study just how important it is.

Here are those 81 state features:

Continue reading "How US States Vary" »

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Life Is Scarce

Matt Yglesias:

Simon (i.e., plenty) for capital and Malthus (i.e., subsistence) for labor. That, of course, is Karl Marx’s vision of long-term economic development. And while I don’t have a strong opinion as to whether or not this is accurate over the long term, it’s certainly a plausible story about the future, and Marx’s solution—socialism—unquestionably seems to me to be the correct one. … If the “robots” are really mere machines, then it should be easy to peacefully divide up the surplus more-or-less equitably, we’ll transition to socialism and everyone will be happy—it’ll be like Star Trek. If the robots are sentient beings, then we’d presumably be looking at an eventual slave revolt and Communist revolution.

Karl Smith:

Is it possible that Health Care is 160% of GDP? What this is telling us, is one way or another health care costs will not continue to rise faster than the overall economy. … The question before you is, do you want the world where health care is limited only by our collective ability to pay for it. What many elites don’t face up to is that if you asked this question to the person on the street he or she might very well say yes. I am constantly aware of this because a persistent source of tension between myself and my family is their feeling that it is not just ok but morally imperative that personal budget constraints be hit in the purchase of medicine. … Making the case for less health care spending is making the case for abandoning the sick and the needy. If you want a world that does not proceed on autopilot you need to be gearing up to make that case. Slight of hand about cost-savings or market efficiencies is not going to do the trick.

When I describe a Malthusian future where most (robot) people wouldn’t live much longer than they were near the best in a very competitive labor market, many readers react like Yglesias, and talk of revolution. Surely, they suggest, no moral person would accept a society where how long folks lived depended greatly on how much they could pay.

Like Karl Smith above, a month ago I tried to make the point that even without robots we are heading toward such a world:

A fountain of youth pill whose required dosage doubled every decade would either have to be banned, or given to everyone over thirty with insurance. … Eventually we’d run out of money to pay for these pills; we’d have to say no to some people, and then they’d quickly die. … Good thing we don’t have a fountain of youth pill, right? Actually, our real situation is worse. Per capita medical spending in the US doubles about every fifteen years, which is still much higher than our economic growth rate. Yet we struggle to see any substantial correlation between health and medical spending – our medicine is mostly useless on the margin. Its nothing like a fountain of youth pill. Our refusal to say no to any medical treatment that seems to our wishful-thinking eyes like it might help will also bankrupt us. And we won’t even get a fountain of youth in the bargain.

One way or another we will find a way to exclude seen-to-be useful medicine from people in our society. The only question will be: what will be acceptable criteria for such exclusion? I’ve argued that the ability to produce enough wealth to pay for your added life is a decent criteria for such a choice, and it can be implemented in an admirably flexible and decentralized way. If you reject that criteria, what other criteria will you substitute, and what price are you willing to pay in centralized regulation and lessened innovation and competitiveness with the rest of the world?

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