Category: Science
Cohort effects and life expectancy and many other facts about the history of American medicine
The cohort reaching age 55 around 1982 (born around 1927) has significantly higher mortality than the cohort 10 years younger. That higher mortality continues through the cohort passing through that age range in the mid-1990s, roughly, when the cohort born in 1933 reaches age 65. That same cohort also has higher mortality when they are 65-74 and 75-84. The story is not one of selection – a handful of less healthy people who die and leave behind healthier stock. Rather, it seems that an entire generation was rendered vulnerable by being born during and just before the Great Depression (Lleras-Muney and Moreau, 2018).
That is from a new NBER history of health care paper by Maryaline Catillon, David Cutler, and Thomas Getzen. This piece is interesting on virtually every page. For instance, on the rise of American science:
Of the 18 Nobel Prizes in Physiology or Medicine awarded 1901-1920, none went to US researchers. Over the next two decades, four out of twenty-four did, then for the rest of the century, more than half.
Then:
…our analysis of Massachusetts data does not support a large impact of medical care supply on mortality in the pre-antibiotic era.
Using the best data I’ve seen to date, apart from RCTs, the authors conclude from their statistical work:
…there is little evidence that access to medical care plays a role in mortality over the entire 1965-2015 period, but it appears to have had an effect during recent years.
That is from p.33
Death rates from influenza/pneumonia and cancer seem most responsive to access to medical care. And I had not known this:
The period from 1935 to 1950 saw the most…decline in infant and child mortality of any time period since 1900. It is unclear how much of this change would have happened without antibiotics, but blood banking and advances in surgical techniques were among the host of distinct and incremental improvements that added to life expectancy while the health share of GDP increased only slightly.
Recommended.
Tom Lehrer, man ahead of his time
Ever since I was a young teenager I loved Tom Lehrer (thanks to Ken Regan, by the way), and I thought I would re-listen to some fresh. I tried the Copenhagen concert, a good overview of his work and with good visuals. I was struck by the following:
1. Lehrer represented the IDW of his day. He said (sang) things others couldn’t, and his main enemy or target was political correctness. It surprised me to hear how little many of the battle lines have changed. Yet Lehrer, while warring against hypocritical political discourse, was in his day on the Left. (Shades of Eric Weinstein!) He worried about the “decline of the liberal consensus,” following the Kennedy era. In 1982 he wrote that he considered feminism, abortion, and affirmative action “more complicated” than the older liberal causes, so perhaps he simply did not blend into the contemporary Left (the piece is interesting more generally).
2. Lehrer’s songs (repeatedly) indicate he saw nuclear weapons and nuclear proliferation as a major problem; in that regard his time probably was wiser than ours.
3. He is very interested in language and the question of how words are used in the public sphere, and how words are used to obfuscate. Might that be the central theme in his thought?
4. He often sneaks China into the cultural references, for instance: “And I’m learning Chinese, says Werner von Braun.” He seems to think it is a much more important country than Russia, although this concert was from 1967 and often was drawing on songs which were older yet.
5. He is much more interested in math and science than current comedians, for instance his “Elements” is a classic [22:54], and redone here with an Aristotle coda, mocking The Philosopher. His audience seems to take this interest in stride. This song is yet another example of inverting what should be said, or not.
6. Yes I know the tunes sound derivative, but most of them are original. And as music…they’re a lot catchier than most of the other musical theatre of his time and I think of many of them as minor classics. I still enjoy hearing them as music. And other than Sondheim and Dylan, how many better American lyricists were there?
7. When he wants to get really gory, he doubles down on mock sadism (“Poisoning Pigeons in the Park”: “…we’ll murder them all with laughter and merriment…except for the few we take home to experiment…”). He once said: “If, after hearing my songs, just one human being is inspired to say something nasty to a friend, or perhaps to strike a loved one, it will all have been worth the while.”
It would be hard to pull this off today. Yet, when I listen to Lehrer, perhaps because I know the historical context, I am not offended. Plus he is flat-out funny. He cited losing his “nasty edge,” and starting to see things in shades of grey, as one reason for what appeared to be a quite premature retirement.
8. He wore a white shirt and his tie was tightly knotted.
9. He’s one of America’s great comics, and the material is idea-rich to a remarkable extent. He hardly ever sung about social themes or person-to-person social interactions.
10. Many of the songs of his that you never hear are in fact commentaries on various folk song movements. Circa 2018, few can understand their references, but they do showcase Lehrer’s extreme idealism.
11. He was at first a math prodigy and later in the mid-1950s, as a draftee, crunched numbers for the NSA. He remains alive and turned 90 earlier this year.
The jobs of the future, circa 1988
A syndicated article published in the September 5, 1988, edition of the Press and Sun-Bulletin newspaper in New York talked with a number of experts about what the jobs of tomorrow would look like. The article first quotes S. Norman Feingold, a clinical psychologist and career counselor who died in 2005.
From the 1988 article:
Feingold envisions a range of exotic careers: Ocean hotel manager, wellness consultant, sports law specialist, lunar astronomer and even robot trainer.
The piece also quotes the George Tech engineering professor Alan Porter who gave his opinion on the future of fast food.
He predicts such innovations as “the Autoburger,” a fast-food dispensary something like McDonald’s, but without human workers.
And the article ends with a mixed bag of good and bad predictions:
Marvin Cetron, a technological forecaster, looks at the year 2000 and predicts a 32-hour work week. “The only job a woman won’t be holding is Catholic priest,” he said.
Cetron said college students of the future will study enzyme research and genetic and robot engineering.
Here is the piece, via Tim Harford. The broad lesson I think is that bets on computers were basically right, and will be for some time to come, and other bets are either obvious or stupid, in retrospect.
Corporate science fiction markets in everything
…the business world has been increasingly aware of the genre’s potential. In 2017, PricewaterhouseCoopers, the professional services firm that advises 440 of the Fortune 500 companies, published a blueprint for using science fiction to explore business innovation. The same year, the Harvard Business Review argued that “business leaders need to read more science fiction” in order to stay ahead of the curve…
A number of companies, along with a loose constellation of designers, marketers, and consultants, have formed to expedite the messy creative visualization process that used to take decades. For a fee, they’ll prototype a possible future for a [corporate] client, replete with characters who live in it, at as deep a level as a company can afford. They aim to do what science fiction has always done — build rich speculative worlds, describe that world’s bounty and perils, and, finally, envision how that future might fall to pieces.
Alternatively referred to as sci-fi prototyping, futurecasting, or worldbuilding, the goal of these companies is generally the same: help clients create forward-looking fiction to generate ideas and IP for progress or profit. Each of the biggest practitioners believe they have their own formulas for helping clients negotiate the future. And corporations like Ford, Nike, Intel, and Hershey’s, it turns out, are willing to pay hefty sums for their own in-house Minority Reports.
That is from Brian Merchant on Medium.
My Conversation with Paul Romer
Here is the audio and transcript, Paul was in top form and open throughout. Yes economic growth, blah blah blah, but we covered many related topics too:
COWEN: And you also think we should simplify the English language. Right?
ROMER: [laughs] Well, there’s two parts to that. One is, in writing and communication, there should be a very high priority on clarity. It’s hard to know what’s the mechanism that enforces that. There are variants on English, like the English used to write the manuals people use to service airplanes, where there’s a very restricted vocabulary. The words are chosen so that you can’t have any ambiguity because you don’t want somebody servicing a plane to get confused. So there are some things you could do on writing, word choice, vocabulary, exposition.
There’s a separate issue, which is that amongst the modern languages, English has the worst orthography, the worst mapping between spelling and sounds of any of the existing languages. And it’s a tragedy because English is becoming the universal second language.
The incidence of people who don’t learn to read is substantially higher in English than in other languages. People have known for a long time, it takes longer to learn to read in English because of the bad orthography. But what hasn’t gotten enough attention is that there’s an effect on the variance as well. There are more people who never get over this hurdle to actually learning to read.
If there were a way to do in English what they’ve done in other languages, which is to clean up the orthography, that could make a huge difference in the variation associated with whether or not people can learn to read English.
And:
COWEN: Can a charter city work if we import good laws from the outside world but not the appropriate matching culture?
ROMER: You’ve zeroed right in on the connection. The real motivation that I had for charter cities was exactly this one that you can see in the US versus New Zealand. You can think of a charter city exercise . . .
This is actually the story of Maryland: We’re going to create laws, and we’re going to guarantee freedom of religion in Maryland, and it’s in the laws; it’s in the institution somehow. That didn’t turn out very well. Maryland had a Catholic elite but then large numbers of Protestant indentured servants or workers. And this kind of commitment to freedom of religion was not stable in Maryland at all.
The case that’s worth trying to copy is Pennsylvania, where William Penn recruited large numbers of people who actually believed in freedom of religion. The word charter comes from the charter that Penn wrote for Pennsylvania, but it wasn’t the document that mattered. What mattered was that there were a bunch of people in the founding population who were committed to this idea of a separation of church and state and religious freedom. And that’s what made it durable in Pennsylvania in a way it wasn’t in Maryland.
And this:
ROMER: …Moses was of this generation that was too enamored of the car, and this is where I think Jacobs had a better intuition. But the challenge, the dichotomy I would pose would be Jane Jacobs versus Gouverneur Morris.
Morris was the guy who drew the grid that laid out the rectangular street map for Manhattan.
We also discussed music, including Hot Tuna, Clarence White, and Paul’s favorite novel, dyslexia, what Paul has learned about management, and much more. Self-recommending, if there ever was such a thing.
Terraforming bleg
Who are the best people working on terraforming and what are they doing?
Meet the Pioneers
Here are the winners from the first Pioneers tournament, summarized here:
In the short 3 months since its launch, Pioneer has garnered a global reach. Our first tournament featured applicants from 100 countries, ranging from 12 to 87 years old. Almost half of our players hailed from countries like India, UK, Canada, Nigeria, Germany, South Africa, Singapore, France, Turkey, and Kenya. Projects were spread across almost every industry — AI research, physics, chemistry, cryptocurrency and more.
They are a remarkably impressive group, here is one example:
Clark Urzo (23, Philippines)
Clark is making a programming language for physics. The idea is to enable anyone who can code to contribute to serious physics research (for example, simulations of gravitating systems). This opens up the field to the wondrous forces of open source and promotes open and accountable science along the way.
Noteworthy: Clark has an insanely impressive trajectory. He learned to code when he was 12. By 16, he was doing Laplace transforms, tinkering with Arduinos, reading Marx and Nietzsche, and taught himself conversational German. He co-founded a VR company by 19.
Or:
Harshu Musunuri (18, USA)
Harshu is creating synthetic materials to improve the diagnosis, treatment, and prevention of sepsis, a leading cause of death in hospitals around the world. Unlike other approaches, these materials don’t require refrigeration and enable low-cost toxin capture in resource-poor settings.
Noteworthy: Harshu comes from a humble background: she was born to an electrical engineer and an elementary school math teacher in a small village in South India. But her work is anything but humble. In her short career, she’s done research with NASA’s JPL, built a seizure detection app for epileptic patients and is now working on a project with the potential to save thousands of lives. She’s also a hacker at heart: when she lacked the formal lab tools to braze at high temperature, she used the exhaust vent of a ceramic kiln.
The overall lesson is that there is a great deal of undiscovered talent out there, and also that some people are out there discovering it! And if you wish to apply to round two, just follow the instructions at the top link.
Open questions from Gwern
This is all Gwern, I won’t add another layer of indentation:
Open Questions
Some questions which are not necessarily important, but do puzzle me or where I find standard answers to be unsatisfying (along the lines of Patrick Collison’s list & Alex Guzey; see also my list of project ideas):
- What is
personal productivity
and why does it vary from day to day so strikingly, and yet not correlate with environmental variables like weather or sleep quality nor appear as the usual kind of latent variable in my factor analyses? Is it something much weirder than the usual kind of latent variable, like a set of zero-sum measurements drawing on a generic pool ofenergy
ormana
? - Does listening to music while working serve as a distraction, or motivation?
- What, algorithmicly, are mathematicians doing when they do math which explains how their proofs can usually be wrong but their results usually right?Is it equivalent to a kind of tree search like MCTS or something else? They wouldn’t seem to be doing a literal tree search because then there would almost never be mistakes in the proof (as the built-up tree of theorems only explores valid inferential steps), but if they’re not, then how are they handling
logical uncertainty
? Are they doing something like MCTS’s random playouts where lemmas are not proven but simply heuristically given a truth value to shortcut exploration and the heuristic is accurate enough to usually guess correctly and this is why the proofs are wrong but the results are right? - Why did Jean Calment live so many more years than other centenarians, breaking all records and setting a life expectancy record which decades later has not just not been broken, but not even approached? Which is extraordinary considering that she smoked, medicine has continuously advanced, the global population has increased, life expectancy in general has increased, and the Gompertz curve implies that, with mortality rates approaching 50%, centenarians should die like flies and ever closer in age to each other and not have occasional enormous permanent 3 year gaps between the record setter (Calment) and everyone since then.
- Why do humans, pets, and even lab animals of many species kept in controlled lab conditions on standardized diets appear to be increasingly obese over the 20th century? What could possibly explain all of them simultaneously becoming obese?
- What happened to the famous genome sequencing cost curve after late 2012, which stopped price decreases, damaged genetics, and delayed the advent of whole-genome sequencing by perhaps a decade? Was it really just the Illuminati’s fault?
- Why do humans have such a large mutation load on common genetic variants? Common SNPs make up a large fraction of variance, even for traits which must be fitness-affecting.
Culture or technology slow evolution
doesn’t wash when human fitness differentials are so large and so many people died young or as infants, and how did the many deleterious variants get pushed up to such high frequencies in the first place? - Why does the immune system so often surface as a genetic correlation or tissue enrichment in GWASes for many things not generally believed to be infectious? Are we missing an enormous range of infections directly causing bad things (or indirectly through autoimmune mechanisms), or the immune system just sort of like intelligence in being a general health trait?
- Why does catnip response vary so much across countries in domestic cats, and also across feline species, with no apparent phylogenetic or environmental pattern? It is so heritable in domestic cats that a genetic reason is plausible, but if it’s adaptive, what is it doing when catnip doesn’t exist in the ranges of most tested cats, and if it’s neutral why can so many closely-different different animals respond to it in different ways?
TC again: There is much more at the link. If you don’t know Gwern, you should know Gwern.
China possible fact of the day
A Chinese researcher claims that he helped make the world’s first genetically edited babies — twin girls born this month whose DNA he said he altered with a powerful new tool capable of rewriting the very blueprint of life.
If true, it would be a profound leap of science and ethics.
A U.S. scientist said he took part in the work in China, but this kind of gene editing is banned in the United States because the DNA changes can pass to future generations and it risks harming other genes.
Here is the full story. Here is further background.
Michael Nielsen Twitter thread of his favorite Twitter threads
Artificial Intelligence is Alien Intelligence
Imagine if an alien came to earth and told us some new scientific fact that no human had ever known. Artificial intelligence is starting to do just that. Computers and AI have long given us solutions to problems that humans could not have worked out for themselves but AI is going beyond optimization to tell us facts about the world that no one suspected. Eric Topol on twitter points us to a paper in Nature that used deep learning to analyze retinal images to predict heart disease–it’s long been known that this can be done which is one reason why ophthalmologists take a close look at your retinas when fitting lenses but not surprisingly the AI can see more than can ophthalmologists. What was surprising, however, was that the AI could also tell gender from retinal images, a fact no one had ever previously considered! As a summary notes:
…that information in a retinal image can be used for the prediction of a person’s gender is surprising and puzzling. This underscores the potential of artificial intelligence to revolutionize the way medicine is practiced and to help discover hidden associations.
*Where Economics Went Wrong: Chicago’s Abandonment of Classical Liberalism*
That is the new book by David Colander and Craig Freedman, here is one short bit:
The best way of conveying our conception of what is at least suggestive of a Classical Liberal stance is to present a handful of economists who, in our view, reflect this attitude. We have chosen six economists: Edward Leamer, Ariel Rubinstein, Alvin Roth, Paul Romer, Amartya Sen, and Dani Rodrik. Each have, in our view, displayed a Classical Liberal attitude to methodology in important aspects of their work.
I am very much in favor of what the authors propose here, although I might reserve the term classical liberal for the more traditional political distinction.
Stubborn predictions
Bayesian theories of perception have traditionally cast the brain as an idealised scientist, refining predictions about the outside world based on evidence sampled by the senses. However, recent predictive coding models include predictions that are resistant to change, and these stubborn predictions can be usefully incorporated into cognitive models.
That is the abstract to a new article by Yon, de Lange, and Press, via Michelle Dawson.
The new movement of geno-economics
By Jacob Ward at The New York Times. Do read the whole thing, here is just one small bit:
The geno-economists seem confident that human genes have a measurable influence on human outcomes. But publicizing whatever predictive power does lie in our genes runs the risk of misleading the rest of us into believing that control of our genes is control of our future. They’re adamant that their motives are in forestalling the dystopian implications of the work, in fighting off misinformation and misguided policies. “The world in which we can predict all sorts of things about the future based on saliva samples — personality traits, cognitive abilities, life outcomes — is happening in the next five years,” Benjamin says. “Now is the time to prepare for that.”
Via Garett Jones.
Science is getting less bang for its buck
By Patrick Collison and Michael Nielsen in The Atlantic:
…we ran a survey asking scientists to compare Nobel prizewinning discoveries in their fields. We then used those rankings to determine how scientists think the quality of Nobel prizewinning discoveries has changed over the decades…
Our graph stops at the end of the 1980s. The reason is that, in recent years, the Nobel Committee has preferred to award prizes for work done in the 1980s and 1970s. In fact, just three discoveries made since 1990 have yet been awarded Nobel Prizes. This is too few to get a good quality estimate for the 1990s, and so we didn’t survey those prizes.
However, the paucity of prizes since 1990 is itself suggestive. The 1990s and 2000s have the dubious distinction of being the decades over which the Nobel Committee has most strongly preferred to skip back and award prizes for earlier work. Given that the 1980s and 1970s themselves don’t look so good, that’s bad news for physics…
Why has science gotten so much more expensive, without producing commensurate gains in our understanding?
There is much more evidence and argument at the link. This appendix provides more detail on their empirical work. Self-recommending, if there ever was such a thing.