Monthly Archives: May 2011

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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Who Beats What

We choose many products mainly as a way to affiliate with its other customers – we often care more about this affiliation than about personally enjoying the product! My evidence for this outrageous claim? Reading a negative online review by a high status person makes us more likely to buy the product:

In our recent … paper “Towards a Theory Model for Product Search”, we noticed that demand for a hotel increases if the reviews on TripAdvisor and Travelocity are well-written, without spelling errors; this holds no matter if the review is positive or negative. In our TKDE paper “Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics”, we observed similar trends for products sold and reviewed on (more; HT Slate via Buck Farmer)

You might know that your mind is capable of both truth-oriented investigation and of delusory pursuit of other goals, but you might think that you can roughly tell when you are in which mode. But if you thought that a negative review makes you like a product less, well then you are more deluded than you realize.

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Don’t “Believe”

Why do people “I believe X” instead of just saying X? Or “I firmly believe in X?” Consider the last ten “believed” claims from featured essay abstracts at the This I believe website:

  1. believes sci-fi gives him a way to connect with his father and sharpen his own intellect in the real world.
  2. believes those regular calls help strengthen the bonds between mother and daughter.
  3. believes it’s important to offer that refuge to her kids because her mother did the same for her.
  4. believes making time to embrace nature gives her the strength to face life’s challenges.
  5. believes we can reach our dreams by embracing our hungers with creativity and passion.
  6. believes the best opportunities for healing may come when no words are spoken at all.
  7. believes he must make time to fulfill more than just the medical needs of his patients.
  8. believes those [sound] waves [from the big bang] are a siren call connecting all of us to the mysteries of the universe.
  9. believes she has found a way to start her journey by focusing on this one moment in time.
  10. believes in the comfort and peace she gets from making bread with those she loves.

In my experience “I believe X” suggests that the speaker has chosen to affiliate with X, feeling loyal to it and making it part of his or her identity. The speaker is unlikely to offer much evidence for X, or to respond to criticism of X, and such criticism will likely be seen as a personal attack.

Feel the warm comfort inside you when you say “I believe” – recognize it and be ready to identify it in the future, even without those woods. And then – flag that feeling as a dangerous bias. The “I believe” state of mind is quite far from being neutrally ready to adjust its opinions in the light of further evidence. Far better to instead say “I feel,” which directly warns listeners of the speaker’s attachment to an opinion.

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Skill Awareness Biases

I’ve posted before on

“Unskilled and Unaware of It“. … everyone’s favorite theory of those they disagree with, that they are hopelessly confused idiots unable to see they are idiots; no point in listening to or reasoning with such fools.

Here is a much better study; it goes a long way to disentangling the effects:

We study … 656 undergraduate students, tracking the evolution of their beliefs about their own relative performance on an IQ test as they receive noisy feedback from a known data-generating process. … Subjects (1) place approximately full weight on their priors, but (2) are asymmetric, over-weighting positive feedback relative to negative, and (3) conservative, updating too little in response to both positive and negative signals. These biases are substantially less pronounced in a placebo experiment where ego is not at stake. We also find that (4) a substantial portion of subjects are averse to receiving information about their ability, and that (5) less confident subjects are causally more likely to be averse. We unify these phenomena by showing that they all arise naturally in a simple model of optimally biased Bayesian information processing. (more; HT Dan Houser)

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On Clark And Caplan

Greg Clark:

[Bryan] Caplan [says] … children impose not costs, but benefits, on the rest of society. …. I think Caplan’s … proposition will soon prove to have applied only in a limited historical window. Future population increases will likely exert substantial downward pressure on the growth of living standards. … There is thus a race between resource costs and scale economies as population grows. … For 99.9% of human history, up till 1800, the winner in that competition was resource scarcity. …

The downward march of food and energy prices since 1800 may well end soon. Current high prices may presage a food scarce-energy scarce future. … Population will again be an important determinant of income. This implies a negative externality associated with fertility, with all the unpleasant implications this holds for those of libertarian persuasion.

Caplan responds:

Clark is right to name economies of scale as one social benefit of population. But he neglects the far more important effect on innovation. … As long as parents are financially responsible for their children, any negative effect of population on living standards is internal to the family. … If parents didn’t care about their already existing children … there might still be a problem. But parents do care about their already existing children. … So it’s unclear whether a problem even exists. … Are you actually willing to bet that global real per-capita GDP will be lower in 2020 than it is today? How about 2030? 2050?

Clark is right that in the long run resource scarcity dominates living standards – that applied for most of human history, and will apply to most of our descendants. Our special dreamtime era of rapid growth and rising living standards can’t last long – probably within a few centuries (and certainly within ten millenia), average consumption will be way down from today.

But Caplan is right that fertility has a net positive externality. Yes property rights are not perfect, so people can hurt each other. But people help each other far more via innovation and scale economies. Yes those will dwindle in the long run, but so will property rights violations.

Of course if you are willing to look only at tiny elites, you can find high average consumption in both the distant past and future. The tiny fraction of future humans who are not robots might well manage to keep a high living average living standard. But most creatures recognizably decended from us will have near subsistence consumption. And that, I think, will mostly be a good thing.

Added 8a: Bryan responds; Tyler comments.

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Neglected Conflicts

We tend to neglect our advisors’ conflicts of interest, especially in immediate face-to-face interactions, and especially when such conflicts are disclosed to us:

Certain study participants were required to make an estimate — evaluating the prices of houses, for instance. Meanwhile, other participants were … given additional information with which to advise the estimators. When these experts were put in a conflicted situation — they were paid according to how high the estimator guessed — they gave worse advice than if they were paid according to the accuracy of the estimate. … When the researchers required the experts to disclose this conflict to the people they were advising. … It actually caused them to inflate their numbers even more.

Experiments focusing on doctor-patient interactions, in which a doctor prescribes a medication but discloses a financial interest in the company that makes the drug. As expected, most people said such a disclosure would decrease their trust in the advice. But in practice, oddly enough, people were actually more likely to comply with the advice when the doctor’s bias was disclosed. … [Perhaps] people feel an increased pressure to take the advice to avoid insinuating that they distrust their doctor. ..

People who are prescribed medicines by personal doctors are less likely to recognize the potential dangers of their doctors’ conflict of interest. … People were more likely to discount biased advice from doctors if disclosures were made by a third party, if they were not made face-to-face, or if patients had a “cooling off” period to reconsider their decisions. … Even if these fixes make disclosure more effective, … transparency is not a blanket solution to problems of corruption. “Regulators should be looking harder at eliminating conflicts.” (more)

As with other products, we may care more about affiliating well with our advisors, especially high status ones, than we do about getting good deals from them.

Yes, regulators who want to help should push to better align advisor interests. But regulators and the politicians to whom they report also have conflicts of interest, and the above studies suggests that voters will also neglect such conflicts. Also, in a democracy regulators hands will be tied by voter perceptions of where the problems lie.  For example, since voters are more concerned about for-profit insurance company conflicts than about high status doctor conflicts, voters push regulators to limit insurer abilities to counter doctor conflicts.  And money-averse voters would probably oppose more extreme ways to use money to align doctor incentives more with patients.

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