Scientific American editors Christine Gorman, Robin Lloyd, Michael Moyer and Kate Wong talk about their recent trips to different science conferences: the meetings of the Association for Health Care Journalists, the Paleoanthropology Society, the American Association of Physical Anthropologists and an M.I.T. 150th-anniversary conference called Computation and the Transformation of Practically Everything.
Steve: Welcome to Science Talk, the more or less weekly podcast of Scientific American, posted on April 21st, 2011. I am Steve Mirsky. In recent days the Scientific American crew has traveled far and wide to cover a variety of scientific conferences. On April 19th, I gathered four members of the staff to talk about what they'd learned. Let's go around the table. Everybody introduce themselves and say the name of the meeting that you were just at last week.
Robin: I'm Robin Lloyd. I'm the news editor for our Web site, and I attended the annual meeting of the Association for Healthcare Journalists.
Kate: I'm Kate Wong. I cover archeology and paleontology for the magazine, and I was covering two conferences last week: the annual meeting of the Paleoanthropology Society and the American Association of Physical Anthropologists.
Michael: I'm Michael Moyer. I cover technology for the magazine, and I was at a conference at the M.I.T. 150th anniversary celebration called Computation and the Transformation of Practically Everything.
Steve: And Christine.
Christine: I'm Christine Gorman. I'm the health and medicine editor at Scientific American, and last week I was also at the Association for Healthcare Journalists conference.
Steve: Kate, talk about the meetings you were at and why they were so interesting.
Kate: Well, there are lot of interesting findings that were announced at these meetings, but two in particularly really come to mind. The first one was the discovery of a massive set of footprints discovered in northern Tanzania. It's about 350 footprints belonging to people, early modern humans, who lived 120,000 years ago. And the footprints kind of fall into two sets. In one set, it appears that people were walking at different speeds and they weren't necessarily doing anything together, kind of doing their own thing; and the other set which is of about 18 people, everybody seems to have been walking in the same pace toward the southwest. And by comparing these footprints with footprints of people living today, the scientists were able to ascertain about how many males and females were in their group, how many children were in the group. And it's really the first opportunity that scientists have had to look at the composition of a social group in early modern humans from that time period.
Steve: This is a really big deal.
Kate: It's a really big deal. It's the only kind of evidence that one can study to get that kind of insight into the structure of a social group.
Steve: Structure of the group, because we see men, women and children; it's a behavioral snapshot as well.
Kate: It is because if they can get a really; you know, they're still some of the footprints are not totally clear on male or female, but once they're able to settle those numbers down, you know, you could start to make some interesting, draw some interesting inferences about behavior. If you have more females than males, for example, which is what they think they have right now, that could lead to some interesting hypotheses about the social structures of people living at that time.
Michael: Wait. You say that the only way you could find out about these social structures is by finding footprints. I'd think that it would be a lot more effective if you were to have some sort of Pompeii like site where you have everyone, just kind of buried under ash in some cataclysmic event, although I understand those are hard to find, but I would think so are footprints.
Kate: Yeah, I guess, you know, what's interesting about this is that you have a moment in time; these seemed to have been formed and covered over very quickly. So, you really do have an identifiable group, as opposed to an accumulation of signals from possibly many different groups, you know, who were buried in a catastrophic situation.
Steve: We'll come back and talk about more from the meeting. Well let's hear from Michael for little bit about your M.I.T. conference.
Michael: So this was less of a standard scientific conference where scientists come and present results for the first time. This was a lot of congratulatory M.I.T. backslapping of, you know, haven't we done a lot of great things in the last 150 years, and with computation specifically in the last 50; which is true. So many of the pioneers of computation actually have been based in M.I.T. But it was fun. You had some presentations that, kind of, dealt with the history of computing, along with shorter presentations from, you know, as I was usually referring to them as the young bucks—the young assistant professors at M.I.T., who are just doing some really interesting new research. It's kind of taken the field forward.
Christine: Where there were any buck-ettes?
Michael: There were buck-ettes, yes. There were a number of buck-ettes, and you, specifically in the younger generation, there were a number of them. Maria Zuber, she's in the geology department, and she was showing how computation and computational methods have taught us a lot about the history of the Earth, and how without these, you know, very high-end computing systems, we wouldn't be able to know and check hypotheses such as the events that came when a Mars sized rock that came and hit the early Earth and split off the moon. But it was all, kind of, how computation has been able to advance geology. And that was the sub-theme going through all these things about how computation is, you know, obviously today isn't just for computation's sake but is really pushing so many scientific disciplines forward. There were people, Eric Lander came up from the Broad Institute, and he was talking about how, you know, it used to be that biology was looking through microscopes and classifying stuff and how he hated biology when he was a young student, so he just, like, couldn't understand why anyone would memorize all this stuff. But now biology is information, that's it. It's doing these genomic sequences, it's finding the patterns and this kind of stuff would just be impossible without the level of computation that we have today.
Steve: Christine let's talk about the health care meeting. I know there are lot of interesting policy and other discussions there.
Christine: There are, and it's an interesting group because the association, of which I'm a member, is an organization for health care journalists, specifically. So the meeting is by journalists, for journalists; and as a result, if you will, there's been some preselection. Everybody who presents at it has typically, you've been vetted by a journalist who thinks this is a very interesting area. And so when I was listening about computational tools, you're actually seeing that in health care as well. When you look at some of the data mining initiatives and so forth. And one of the speakers Don Berwick, who's head of the Center for Medicare and Medicaid Services, talked about a new Web site, healthindicators.org, I believe it was, where the CMS is making available information about health outcomes for particular areas in the country, and so you can actually look up health outcomes in your neighborhood. There's a lot of this that's happening now. There was a report earlier this year, where all the counties in the 50 states are ranked within their state by their health measures and, of course, I immediately had to look up New York and the top county is Putnam and the one in last place is…
Steve: Where I live—the Bronx. (laughter)
Christine: But anyway getting back to the association, so there was certainly a lot of technology, data mining with the Berwick talk, but also some very intriguing social sciences. One of those panels that I went to was on gunshot wounds in children in a neighborhood in Philadelphia; and I was just astonished at the mapping software that is used to try to understand on an epidemiological level why do some kids get shot or what increases your risks of getting shot. I think, you know, obviously sometimes it's just being in the wrong place at the wrong time.
Steve: And some places are by definition the wrong place.
Christine Some places are by definition the wrong place to go, and it's not necessarily the entire neighborhood, it's maybe one part of the neighborhood or the wrong time in that part of neighborhood. And what the researchers did was they took mapping software—even more complex than and more detailed than what you can get on Google Maps—and with every kid who was brought into the emergency room at this one particular hospital, worked with them to trace their path over the course of the previous of the 24 hours prior to the gunshot. And so they had, and it's interesting because the kids clearly understood how to project from above, you know, and still understand. And they would trace, well from this time to that time I did this and I did that, and then they trace their whole path and find some very interesting links, including that hearing gunshots does not increase your risk of being shot. Seeing somebody knifed increases your risk of being shot and increasing exposure to alcohol over the course of the day dramatically increases your risk of being shot; or these kids' risk of being shot. And so a lot of very interesting ways of using data both, you know, the genomics, the stuff that we're used to. Francis Collins, the director of the NIH was there talking about cancer genes and mapping cancer genes; but also mapping, you know, geography and relating that to health over time. So it was a fascinating conference.
Steve: That's so interesting because you know, the most famous example is probably still John Snow and the cholera and…
Christine: And the pump handle.
Steve: …the pump handles and using maps of London to figure out exactly where this cholera was coming from. And he did.
Christine: What's interesting about that is that people disbelieved him. And he very carefully and very meticulously by hand drew maps and put dots of all the cholera cases and then looked at the wells. Because in those days people didn't have tap water in their homes; they would go to a well and pump it, you know, pump the water up from underground; and what he was saying some of the wells were contaminated, and he then proved it after he identified from his maps which wells exactly were contaminated, he proved it by taking the pump handle off the well. And then all of the cases of cholera in that area suddenly dropped dramatically. So that was the proof, and you know, the use of maps in epidemiology in many ways has been very arcane, in the province of totally specialized experts. But now it's happening with some of the new data that's being made available, the databases that are being made available by the government and by others. Any one can do this kind of mapping for their own neighborhood and, in fact, one of the panelists at the conference was, in effect, teaching journalists how to use databases and maps. Because the thing about a database is if you looked just at the data, it's not visually very interesting or you don't really see things. But once you mash the database with the map you can make all kinds of correlations and conclusions.
Steve: And the other thing you're talking about, the outcomes, that could be of real interest to the general public. Because, let's say you need to go in for a hip transplant, you might check out your hospital, but you might also want to see, maybe you live in a place where you just really don't want this done. It's worth it for you to travel to someplace else in the country where the outcomes are significantly better.
Christine: Now that's an interesting point, Steve because we often, sort of, get tunnel vision thinking only about the operation, but you have to remember that's an entire episode of care—which is the new phrase du jour—that it's not just about the operation; it's also about the aftercare, the care you get at home, and so it's really, in many ways, the nursing care, the transitional care afterwards, the preparatory care beforehand. And so just focusing on the piece in the operating room doesn't make sense. Even there, you'd have to think about well what are the infection rates in the hospital? That was something else that Don Berwick talked about at the meeting that this information about hospital infection rate, so; let me be clear, that term, the technical term, is hospital-acquired infection. And basically, all of those are, theoretically at least, preventable. You should not get an infection just by going into the hospital and being treated. Now there are reasons why that happens. But CMS, the Medicare system is starting, has put hospitals on notice that they have to get their infection rates down, and if they don't they're not going to get reimbursed. Eventually, so now that information is coming in anonymously, if you will, you can't find out which hospital, but eventually that will become public information. So there again, a use of data to let people know, you know, what's going on in their community.
Steve: Or you can really just acquire as much data as you want now, throw it into computers and have stuff fall out that as you said, wouldn't just see by eyeballing.
Christine: And yet there is a funny kind of data overload that can happen because if you can't measure it, then you don't always see it. In some of these things, so it might be easier to measure infection rates, for example. But how do you measure the kinds of relationships that happen in a neighborhood that keep kids healthy? There's a county in Kansas—Wyandotte County, part of Kansas City—Kansas that has the worst health outcomes in the state of Kansas. And their mayor, Joe Reardon, has just launched this whole campaign called, I think, it's Healthy Neighborhoods Initiative or something like that. They want to see what they can do to make people healthier because they already have fairly good access. There's the medical center in Kansas City, and there's a number of clinics and things like that. And what they found was they needed to improve the availability of affordable fruits and vegetables in poorer neighborhoods; education starting well before kindergarten in terms of various cognitive things; and then health promoters, that is to say, not technically trained, but nonetheless, somewhat trained people from the community who teach their neighbors to how to pick the right foods, how to follow up, you know, how to take antibiotics so that you don't stop at the end; you know, all the things that help promote health. Sothere's very good research to show that most of the difference in health outcomes is nonmedical in nature; that only about 10 percent has to do with access to health care and the other 90 percent has to do with education, literacy, the relationships that you have, diet and the structural environment, how safe you feel. It's great to tell people to exercise, if they can't go out…
Christine: …because they're afraid of getting shot. (laughter)
Steve: Right, right.
Robin: Well, that human angle that you're talking about in terms of health care delivery and then the web, the connections among various parts of the system and people's social worlds was reflected in the session that I wanted to talk about, also at Association of Healthcare Journalists. And the session that, one of the sessions I liked and I wrote about was neat because one of the main speakers was David Blumenthal, who was with the Obama administration for the last two years, and he was hired to be the National Coordinator for Health Information Technology. But basically what that means is he was hired to implement that part of the stimulus that was allocated toward electronic health records or electronic medical records. And there's a move toward calling them electronic health records because it indicates more so that they're available throughout the health care system and industry and that there're a piece of data that we can use in aggregate to start to look at trends across the population and trends in regions; when the flu comes in for instance or something like that, or adverse drug reactions can be tracked if we are all entering our data in electronic health records. So anyway the surprising statistic that Blumenthal gave us was that at this point—you know, I found this interesting because I've been reading various stories on this beat for the past couple of years saying that there's going to be lot of technology problems; this is a huge information technology problem. And he said, actually the problem is going to be human, it's going to be cultural—getting physicians and health care systems to implement the software and to embrace it. And the stat that I just forecast I would tell you is that he said that about 20 to 30 percent of all primary care physicians in the nation are now using at least a basic electronic health care system. And this is a big deal. Because, I mean, just at the individual level, the private practice level, it costs you probably around $100,000 to set up one of these systems, not to mention 20 hours of training, one of the speakers said. So, on the other hand, there were some stats given out at the session that said well, you know, first of all there's some kind of incentive that the bill provides for taking this leap right now—later, there'll be penalties, starting in I think 2014 or 2015 for not implementing electronic systems—but at any rate, right now the incentive is anywhere from a $40,000 to 60,000 dollar rebate. And then also there was another stat saying that folks who implement electronic health care systems are having an increase, a net increase, in their income, at least at the private practice level, of $40, 000 a year, which is significant. And that just comes by virtue of billing for things, you weren't billing for before because you're logging every single procedure that you are undertaking; and in the past there was so much talking and communicating that, you know, you didn't write everything down. But that entering of the data of each procedure that's conducted triggers the billing, and so there's better accounting done and then better income.
Michael: So the lesson is that electronic health records drive the cost of health care up?
Robin: Exactly, (laughter) you're right. And that came up actually in another session I went to which was basically looking at the Massachusetts health care reform effort, which has been fairly successful in terms of delivering health care but has done nothing to address costs. There are no incentives built into that bill—into that law that's been in place for four years now in Massachusetts—to drive, you know, to incent hospitals and private practices to reduce their costs. However, the ACA, the health care reform that's been passed at the federal level, does have some measures that incent hospitals and private practices and health care systems to reduce their costs. So there's some hope for that and, you know, it's also good to know that one of the things that came up at the sessions is that the federal reform bill is largely based on the Massachusetts reform bill. So, it's very interesting, and that's why we're kind of focusing on this session to look at how, what they did right, and what could be done better in Massachusetts. And actually it was pretty hopeful about what might happen once implementation unfolds further at the federal level, because they have had such success with coverage of the population. There are only 3,000 children left in the State of Massachusetts that are not covered now by health insurance. They went from 94 percent—already they were a leader in terms of having people in the state insured—and they went from 94 percent to 98 percent coverage. So, you know, it's a question of controlling costs in the future, helping people find primary care is still a problem in the State of Massachusetts, and trying to figure out how to find a way to help the people who fall through the cracks. There was one example given by a public radio reporter of a maid whose employer did offer insurance—she made $1,500 a month and the insurance would've cost her $1,600 a month. Well, obviously she's not going to take that option. She's able to appeal to the state and not get assessed with a tax penalty for that, but you know, there are still those cases.
Steve: A different way to look at it with the same numbers, because it might not seem that impressive if you've gone from coverage of 94 percent of kids to 98 percent of kids.
Robin: Well, that's the whole population, by the way.
Steve: The whole population, okay. But the other way to look at it is of the uninsured, now two-thirds of them are insured.
Steve: That's just a more positive approach to the same numbers. And you have a blog item that you've published on the Web site about the medical records.
Steve: So people can look for that on our Web site. Kate, some human evolution stuff at the meeting about the arguments about some new fossils about whether it's a direct ancestor to Homo sapiens or whether it's an Australopithecine. What's going on with that.
Kate: Yeah! The most exciting news to come out of the two meetings I was at last week concerned some fossils that were announced from South Africa last year. There are two partial skeletons dating to around 1.95 million years ago and they were assigned to a new species of human—Australopithecus sediba. And it's a funny species because it has a lot of traits in common with Australopithecines, like Lucy, who lived a little more than three million years ago, and early members of our own genus, Homo. And so what these guys said last year was that, "Hey maybe we have, finally, a good candidate species for the, to be the ancestor of our genus." And this year at the meetings they released results from new analyses that they've done that go a long way toward supporting that assertion. They were two really interesting findings that are, kind of, linked. One is that when they analyzed the skull and the shape of the braincase to get an idea of what the brain of this human ancestor looked like, it's really tiny. It's only about 430, an estimated 430 cubic centimeters, which is about a third the size of our own brain. And yet at the same time, there are parts of its brain, particularly their frontal lobes, where the organization is very much like what you see in early members of our own genus. So it's a weird tiny little almost chimp-sized brain, but with the very human like organization in a critical part of the brain.
Robin: How can they see organization in a fossil?
Christine: (overlapping conversation) The lobes in the brain, kind of, make dents on the inside of the skull.
Robin: Okay I was just curious.
Kate: Yeah, I just can't say more than that, than you just see the shape of that, the external shape of the frontal region. But yeah, basically what they do is they CT scan and synchrotron scan these skulls and then make these virtual endocasts, they call them, to show what the brain looked like. And so you've got this tiny but advanced brain in Australopithecus sediba. And then at the same time, when they were looking at the pelvis, and this caused a big stir at the meeting, so there's been this idea that Lucy's species, you know, the changes that you get in the pelvis from the last common ancestor of humans and chimps were to, sort of, make us good at upright walking; and then further changes to the pelvis that you see in the evolution of our genus which will accommodate babies with larger brains. Now the weird thing about sediba is, it has a very human like pelvis but it has a tiny brain, so obviously something, some kind of other selective force is acting on the pelvis that has nothing to do with the expansion of brain size that you see in our genus. So that's going to be a topic of discussion for months and years to come.
Steve: So the pelvis looks like it was, you know, condition ready to accommodate a larger brain.
Kate: Exactly, exactly. And yet we know that the babies must've had small brain because the adult size is so tiny.
Christine: And evolution isn't predictive, it's really only reactive. So when you think about our pride in our brains, you know, in our big brains and then here is a case where, well actually that's an accident and you know, wasn't the increasing size of the brain that gave the pressure that women with larger pelvises that could accommodate bigger heads survived.
Steve: Right, it's as if the pelvis was there and so children with larger heads and their mothers with the genes, apparently, for producing children with larger heads both survived, but only because that larger pelvis was already there.
Kate: Was already there for some other reason.
Michael: So this is like the big chicken-egg problem of early human evolution—which came first? (laughter)
Steve: Is that the brain or the pelvis?
Kate: Yeah, well I mean, they always thought that the two, kind of, went hand in hand because the available fossils of early Homo all had the modified pelvis and a bigger brain. So they just figured that they, you know, basically went together. And now you have the Homo-looking pelvis but the small brain.
Michael: Is the implication that we first walked upright and then got a big brain?
Kate: Well, I interviewed one fellow who wasn't part of this analysis and his thinking was that, you know, this may suggest that—and this has been a debate that has been going on for years—that, by the time you have Lucy, you have a hominid that walks upright a lot when it's on the ground, but still spends a fair amount of time in the trees. So that's phase one. And then phase two—which is presumably I guess when you get Homo in this scenario you are—your pelvis has been so modified, I mean other features too have been comparably modified, you're no longer, abandon life in the trees, and you're a dedicated bipedal creature living on the ground.
Steve: And you also at the blog item upon our Web site about this, right?
Kate: Yes, I have a blog item on the footprints and a blog item on these new findings about Australopithecus sediba.
Steve: Excellent, anything else from M.I.T. that you want to talk about Michael?
Michael: You know, there was just such a wide variety of presentations, it's hard to kind of narrow it down into if there were any real big overarching themes. I mean there are people there from the world of finance talking about the big problem that we had last year with the flash crash, which people remember was in the course of 13 minutes on the stock exchange, there was some sort of correlated automated trading strategies which drove down the price of various stocks from 35 dollars to 3 cents. You know, obviously this was not people sitting there on the phone yelling to sell, it was the computers saying "Oh boy, well if this happens we should all do it." And this is a big problem, because so much of our trading now is automated.
Steve: And we are talking on the anniversary of when Skynet takes over the Earth.
Michael: That's right.
Steve: April 19th, according to the Terminator movies.
Michael: Of this year.
Steve: No, well, maybe it's this year, I don't remember. But it's this date anyway.
Robin: And market dropped about 200 points this morning, too. (laughter)
Michael: Well it's, it is all the computers' fault (laughter). I mean it's true. Well they're now trying to figure out ways to come up with more, I guess, better circuit breakers so that these sorts of things don't happen. And what would be best is that if we, kind of, knew the ways in which all these various firm's trading strategies were correlated, which we don't know because these trading firms do not divulge their strategies because that's their core intellectual property, and the way they make gazillions of dollars all the time online. And so there was some talk about being able to use new advances in encryption at the meeting so that you would be able to kind of analyze people's strategies to look for these correlations without necessarily exposing the strategies themselves.
Michael: Which I thought was really kind of interesting.
Steve: You know, today is also the anniversary of the death of Darwin, speaking of the human evolution with Kate, and just to finish up—am I wrong, but isn't the place you're most likely to find a fistfight at a conference, one of these human evolution anthropology conferences where people are arguing over whether that bone represents a new species or just an example of a known species or whether some artifact is again a new species or some kind of pathological example of an old species?
Kate: Yeah, you know, paleoanthropology is sort of infamous for its fights. There was one heated argument that I witnessed at the meeting, and it concerned whether some marks on animal bones that are around 3.4 million years old from Ethiopia, whether or not they are actually butchery marks inflicted by members of this new species or whether they're just the result of having been trampled by herds of animals. And so there were some heated words exchanged there. That's the only fight I saw, no that's not the only, that's the only one I want to talk about. (laughter)
Christine: So what did they do? How do they fight? I mean do they use…
Christine: Yeah. (laughter)
Kate: Yeah, it's ugly. But you know I mean this, a really, sort of, interesting dynamic that was in play at this meeting which has to do with those Australopithecus sediba fossils was that the leader of that project actually brought casts of all the fossils that they have recovered and prepared so far to the meeting, even ones that they haven't described formally in a journal yet; and was letting anybody see them, inspect them, you know from students to, you know, people with conflicting ideas and that is something you just do not see everyday in this field. And it was just really interesting to hear so many people singing the praises of this guy, Lee Berger, for being so open with this material. It's a real change.
Steve: So people share their interpretations or raw data; when they're publishing a peer review article obviously the peers need that stuff to review, but they don't show the actual fossils to each other.
Kate: Sometimes they don't even show the casts to each other.
Steve: Well I would like to thank you, my cast of experts here today—pretty smooth, huh?—(laughter)And we look forward to more meeting updates to come.
Steve: Thanks for sitting in on the conversation. We'll be back next time with the peek at what's behind door number 1 or 2 or 3 as we look once again at the infamous Monty Hall problem. In the meantime get your science news at www.ScientificAmerican.com, where you can check out the blog items and other reports written by the members of the roundtable you just heard about the subjects we discussed. You can also check out our new In-Depth Report on the science of tornadoes.
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