First of all, I'd like to thank the organizer who allowed me to give a talk here. My talk is a little bit different from the previous ones. I'm talking about the some population problem, but it has a long range of connections. The list of my talk is this much, but I don't know how to do, you know, 30 minutes. Okay, so let me first introduce some previous work. In the spin system, as you know, that the soulless propose some wonderful R-square interaction. And when the interaction between the two ising spins, even like this, the very different behavior occurs, and the transition becomes discontinuous even in one dimension, and the correlation lengths diverge with some essential singularity like this. So it exhibits hybrid mixed order phase transition. Recently, in this paper, they are a little bit modified of the original model. The interaction is given within the same domain, and in that, for this case, the correlation lengths diverge in a power-low way, so this case also exhibits the mixed order transition. Let me first introduce the, we are going to consider the similar problem in population. So population transition, population model was introduced to illustrate the metal and insulate transition. At each site who are born randomly pick up, and that this site is occupied by metal, and the fraction of metal is given by P, and P is a control parameter. If we increase the control parameter P, then there is some critical threshold value at which the spanning cluster is generated that is connected from top to the bottom like this. So in the population problem, the order parameter is the fraction of occupied site belonging to the spanning cluster, which exhibits the second order transition with the exponent beta. In one dimension, because if one site in one dimension is missed, then there's no connection to the spanning cluster so that the PC is equal to one, so it's a trivial case. If the dimension is higher than the upper critical dimension, then we can ignore the lattice structure so that it reduced to the so-called random graph model introduced by Erdisch Rayleigh. The system, the N node exist, and we pick up two nodes randomly and occupied with the probability P. So in actually, we select two nodes and occupied P and so on, set that, so the control parameter is number of links L, and the T is defined as L over N, and which is equivalent to the mean degree over two. So this is the control parameter. And then this model exhibits also the population transition and with the second order behavior. In long time ago, about 1983, Schurman introduced some long-range connection population problem. So with the probabilities of the I side and J side connected with a bond with this probability, here the control parameter is a P and S. When S is between one and two, like this, then there exist some finite special value, PC. Above PC, there exist some, the spanning largest cluster, the P. And when the S larger than two, then PC equal one, so there is no giant component in the system. So P infinity equals zero for all P. So in this problem, we have some long-range link between the I side and J side. So this problem already is different from the long-range interaction problem we discussed so far. And later, the Eisenman and Neumann consider this model. So for given finite distance L in one dimension, and if the PI side and J side is occupied with the PIJ with this probability, then there exist the control parameter beta here and P, beta and P. So if beta is larger than one, it can be a population transition, but this is a discontinuous population transition. And so the jump is larger than some beta one minus one half here. So but beta is less than one, then there is no finish transition, and so there is no spanning curse. Later in one dimension, these people already remodified here, instead of P, here is one, and then by this short-range connection, the population transition occurs, population cluster occurs. So in this problem, in this paper, they are interested in the diameter of the population cluster in actually in T dimension. So what they obtain is that S is equal to one. They calculate exactly analytically for one-dimension case, but also they extended and conjecture for general dimension D. That's when S equal D, the diameter proportional to log N over log log N, but in this region, diameter proportional log N to this delta, delta is larger than one, and for this case, it's a power low way, and omega is less than one. Interestingly, when we construct the energy range graph, the mean distance is proportional to log N, also for the scale-free network case, when the degree displacement is even proportional to K to the lambda, lambda is the degree exponent, and when lambda is between two and three, the mean distance is log N over log log N, but lambda is larger than three is log N. So this result, and maybe it has some split, shared a split with this case. So we are interested in some physical characteristics of the problem, one, the energy range model, for them. Let me, so in this case, the long-range connections plays a very important role to determine the types of phase transition. Let me first introduce some epidemic model, so-called SIR model, so susceptible-impacted node, recovered model, so each node on the energy range graph can have three states, susceptible state or impacted state or recovered state. When as a node of labor nodes in susceptible state and impacted state, then this susceptible state becomes impacted by this node with a probability kappa here, and later the impacted node becomes recovered node, so it's no longer impactious. So, for example, this node is impacted here, and this node becomes recovered, then the dynamics stop and no longer proceed next time. And then, we are interested in the SIR model, on the populating cluster, not only at the critical point but above the critical point here, and then for this case, the dynamics proceed in the critical branching tree so that the mean branching ratio becomes one here, so mean branching ratio will be one, so that it usually population transition like this when at the critical point kappa C. At this point, the number of nodes with the state R is scaled as n to the third, so when you normalize it, then it's n to the minus one third, so that in some of the limit, it becomes zero, so ordinary second order transition occurs. So, however, when you introduce some intermediate state W between the susceptible and the infected state, then, so this is the susceptible, weakened and the infected recovered model, so-called SWIR model, then the transition becomes a hybrid transition like this. There is some discontinuous jump and continuously increase. Here, the control parameter kappa, new and lambda, but kappa is most important. So, we can draw the very complicated phase diagram analytically but I would like to say just to say that when the, there is just some critical value of mean degree, kappa k mean degree above which the phase diagram looks like this, so that there is some R A, kappa C. Below kappa C, the order parameter zero and jump like this here and continuously increase. However, when the mean degree is less than some threshold value, then this m star here, m star becomes zero so that the discontinuous transition does not occur. The susceptibility for this case diverge here like this. For this case, here's a, we start with an impact node here, then with some probability kappa with the neighbor, one of the neighbor becomes, a neighbor becomes infected like this, a neighbor can become the weakened state like this and so on. And then after a long time later, so a long time later, impacted the node here and then its neighbor becomes a W node here because there is a long range connection in the Erisirini model when the mean degree is larger than the threshold value. Then there's a long range look here. So this, by this long range connection, this weakened node becomes infected node and so that the process, branching process behave like this. So up to some time here, the critical branching process this way, the impacted node here, critical branching process dynamics proceed up to some here and equal, this process decay like that. However, of long time later here, the W plus i becomes two i process begin to occur here like this process so that the infected node added by this process so that number of recovered node increase above the one, this is a mean branching ratio. So it becomes a super critical behavior so that the order parameter, the order parameter increase suddenly by that super critical behavior like that so that hyper hybrid transition occurs here like this. So that formation of the long range look like here, the major place, the connection of the long range link plays an important role to make the hybrid population transition. Okay, this mechanism can be applied to other hyper hybrid population transition, for example, k-core population. So here is also the W i to the i to the i, two i process increase later here and by this ingredient so that it becomes a super critical behavior so that for k-core population case, there is a sudden drops of the order parameter, these drops is induced by this process like this. So the dynamics is almost the same as I told you for the SWIR model case. Also the cascading failure models in mutually connected multiplex network dynamics case also exhibit some sudden drops of the order parameter. The dynamics can be explained in the same way we as we talk in the SWIR model. So that such a long-range look connection made and so that the order parameter collapse suddenly. Let me move on to the another program. So for the, can we obtain the discontinuous population transition using the Edo Shireini types process. That's the so-called explosive population model. Okay, explosion population model. In 2009, these people introduced some so-called explosive population model. They modify the Edo Shireini model slightly. In the Edo Shireini model, two nodes are pick up, two nodes are pick up randomly and connected by link. But for the data model, we select two pairs of nodes, this one and this one. And we have to determine which node indeed connected to the system. So if we connect this link, then the resulting cluster size is smaller than this one so that we connect to this link and we discard this link and so on. And then they said that there exist some discontinuous transition like this beyond the population threshold of the ordinary population and here, right there. However, later, this continuous behavior valid on only the final system. In infinite system, this jump reduce so that it becomes discontinuous transition. So in the later paper in science, the local modification of the Edo Shireini model cannot guarantee any discontinuous transition. So we need some global types information, modification is needed. Later, our group introduced some so-called avoiding spanning cluster model in each the dynamic rule is defined as follow. So in two dimension, for example, we select two candidates of bond B1 and B2 here. So for example, if we occupy this link, this bond, then there is some spanning cluster between left and the right so that we avoid this B2 link bond instead of B1 link here. So if we occupy the bond B1, then there is no spanning cluster between left and right. And we proceed this dynamics to go on and then at certain time to see that we choose the two bond B1 and B2 and both make the cluster connected so that we have no choice but to choose one of them. So the spanning cluster is generated this way. In this case, in this case, this continuous transition occurs this way. When the M, M, this is the case M equal two. However, we can generalize the four M case. Then if M is large, then then this, there is this, some discontinuous transition. When M is large, that means that we need, we have enough information, global information to choose the one which one is the best way to suppress the spanning cluster. So analytically, we show that when M there exists some MC value, which is proportional to global M, when is M is larger than MC, then global information is enough to guarantee the discontinuous transition. So to get the some discontinuous transition, we need global long range information and we can make some spanning cluster discontinuous. Recently, we introduced another model to show which shows the some hybrid population transition. Again, this model contains some global dynamic rule. We classify the node into two subset. Here, the R subset means that in this subset, the cluster with the smallest size contain. So total number of fraction of the node belonging to this subset is given by G. G is some control parameter, G is less than one. So when G is equal to one, this model reduced to the Erdos-Reynm case. So dynamic rule as followed, at each time step, we pick up the one node from the entire system. So here or here, and the another node is chosen from only the smallest subset, right here. So two nodes pick up from one here and here, then this cluster is merged. If the size is larger than this border, then this cluster will be here like this. And then the hybrid population transition and the cluster size distribution at the transition point follow the power row and the exponent tau depends on the control parameter G. So we can find that the critical exponent of the hybrid population transition depends on the control parameter G. So this hybrid population transition shows the continuous varying, has the continuous varying exponent value. Like for this case or the previous case, for the hybrid population transition, does not guarantee the hyperscaling relation. So okay, this is the summary. I stop here, okay. Thanks a lot for nice talk, so out there.


With love you can help people turn the corner and love means not backing away and You'll find your own way to do it and It might be that you need to ask a question like I Want to be here to support you. I want to love you Tell me how I can help and it may be that you just need to follow instructions But if you can If you can find it within yourself not to back away That very act Will mean so much. I would imagine for the other person So try let's try


I sincerely apologize for the condition of my voice. I took a trip to Guangzhou after arriving, and somewhere between Hong Kong and Guangzhou, I lost my voice. I will do my best. I've never in my whole life had this experience. Cities clearly are often analyzed as breeding grounds for the transmission of disease and places with disproportionate health and risks, as we have seen. And the literature often refers to this as the urban health penalty. Now, what's striking about what I've been hearing, what we've all been hearing this morning, is that cities clearly may often serve as entities for the protection and the promotion of population health. That's the optimistic view. I would add that cities could also serve as critical... How do I push the slides, by the way? Green light, okay. I would add that cities could often... might also serve, and this we've not discussed, although we have, the mayor has given an example, as critical governance entities for strengthening the health care system in a more integrated sense. Now, I know that this audience would agree that a population's health is largely affected by what has come to be known as the social determinants of health, which, of course, for our purposes here today includes the physical environment, climate, and even what will be a focus of this conference, the built environment. And this is not a new topic. There are important books that have come out by economists on why some people are healthier than others, by epidemiologists, Sir Michael Marmot, on the role of positioning in society and how that affects health on the built environment and the influence of physical activity. And there are WHO commissions working actively on all the issues that we've been hearing today. Now, these relationships are obviously very complex because health and well-being are a bit like the sky. They cover everything. They touch nothing. So it's difficult to grasp and attain. And yet we know certain behaviors increase the risk of mortality. For example, smoking is influenced by culture, by role models, and by public policies. We also know that when a population is overweight and obese, it raises mortality risk. We know that physical activity, even a little bit every day, can reduce the risk of chronic disease. We know that certain forms of urban development, suburbs with commuter populations dependent on cars, serve to limit physical activity and facilitate obesity. We know that neighborhoods with poor, disadvantaged populations are disproportionately exposed to environmental toxins and suffer from lack of access to healthy amenities ranging from childcare to food markets and convivial places in which to enjoy life. So it's not surprising the famous story about the Jubilee line. That's not a London uniqueness. That's true in New York. That's true in every major city of the world, except perhaps the one that the mayor represented today. Perhaps, and I would wish to look further, because he's clearly made great progress, but I suspect one might find, even in Lopredo, a hierarchy of life expectancy based on place would be an interesting research project. It's certainly not unique to London, although I take that to be an example of British understatement. I would go on, but I'm sure that we will discuss later, and I would add that there are also complex relationships between all of these characteristics and not just population health, but the health care system, which are very much not explored and not dealt with generally. In the field of urban health, we typically talk about poor people in cities. We rarely talk in the field of urban health about how to make the entire city a more healthy kind of place. There's a sense in which viewed from very far through a telescope, the health of cities remains, in fact, I would argue, invisible. Look at these cities, and I don't mean to compete with Ricky Burdette or any of the other ones here because I'll never have such nice slides, but look at Tokyo or Paris or New York or London or Hong Kong when there's no smog. All of these cities claim to be healthy cities when looked at from afar. No mayor of any city stands up and says, I represent an unhealthy city. All cities through a telescope are healthy cities. They look healthy. The trouble is that you can't judge a city from the photograph so far away. So as Ricky Burdette has also done and as you have to go inside and look at neighborhoods, this is central, upper central Harlem. Much of Harlem has greatly improved. This is just 20 minutes from where I live in Manhattan. And this is not a nice place in which to grow old. It's not a nice place in which to be born. And it's not a nice place in which you can have a healthy life compared to a neighborhood like this where you have a health club on every block, even though this was designed to show that the built environment matters and you shouldn't have an escalator for a health club. Now a few months ago, I thought of Ricky Burdette as I looked at the first page of the New York Times. I don't know if you saw that, but there was a picture on the front page of the New York Times of the South Bronx and a development which I did not know about, but perhaps you did, called Via Verde. Via Verde. In the South Bronx, a very, very bad neighborhood was a new subsidized rental housing for low and moderate income residents. Designed as a healthy place to live with a medical clinic on the ground floor and courtyard above, with a big health club. It's oriented to get maximum natural light. Staircases designed in such a way that when you go down, you had extraordinary views in order to encourage walking and discourage elevators and on and on. And I think that that is wonderful. I think more of that should be done. I think the built environment is important, but I recently reviewed Jason Corburn's book on healthy cities, which is a document everyone here should read through new publicity. And he says, now I might get him in trouble, that one might call this sort of project an example that is the built environment of moral environmentalism. The belief that rational physical designs can change the social conditions of the poor. And I would suggest at least that that's optimistic. Back to the question of cities. How well does Hong Kong do? Are these data visible? Barely. Typically epidemiologists and health people look at these kinds of data and we've seen some of these data. We can look at infant mortality, life expectancy at birth for males, life expectancy at birth for females, life expectancy at 65. Some people think that at 65 it's a real test of medical care. Other people think it's still determined by the social determinants. These are interesting data and this is the way we typically measure this, but it's more helpful than telescopic photographs, but it's still not that helpful. It does tell us that Hong Kong is doing very well. There's no question about it. Now I have slightly different numbers than those you saw before because to make comparisons is very difficult and I'll go into that a little bit later. So to get comparable data for comparable cities you need to go back for comparable years so all of this is out of date. But at least during the early 2000 period there was no great dispute that Hong Kong was maybe not always the best but in some respects the best and I have no time to spend time on the table but you have it in the newspaper article. Now what's interesting is that these are so-called objective data but when you look at self-reports based on surveys and it's very hard to have comparable surveys but one of my pitches today would be that we urge cities of comparable nature to conduct comparable surveys to get to the bottom so that we can't, we stop talking all the time about how we should make cities healthier and start talking about which cities are in fact healthier based on serious information rather than n'importe quoi as they say in French. When you look at these self-reports it is consistent, entirely consistent with the objective data. We have in New York more hypertension, more diabetes, more high cholesterol, more asthma than in Hong Kong but we couldn't get comparable data for London even though there's a boost and it exists but it's not quite comparable. So we are working on that but perhaps some of you don't even trust self-perceived health as a measure of anything so if you look here at self-reported health status and you ask Hong Kongers how many feel that they are in excellent health, less than 1% whereas in New York almost 10% say they are in excellent health and it's the opposite. When you look at those who say they are in fair to poor health in New York City only 38%, I'm sorry, in New York City yes only 38% and in Hong Kong 60%. So that gives pause about the nature of surveys or it may reflect questions of culture, all kinds of interesting questions behind that. Clearly we do need more research not only on processes and what should be done and how much more intersectoral work we need to do. We need more research on actually disentangling the relative impact of all these different factors because even if you believe, as I believe that the powerful lever is made of social determinants, environment, there's no question about that. Behavior, which is socially conditioned, there is still, and here is where I join some of the medical establishment of which I'm not a part, there is still some role, some role 25%, 10%, I think it's less than 25, it might be slightly more than 10 for the organization of healthcare delivery and that raises questions of how that ought to be done in cities and in a very, very brief amount of time I'll just do a kind of station identification to show you some of the work which is really emergent and not at all complete, even though we've published a few books on comparing cities and we have a very biased sample. We took the largest cities of the richest countries of the world, much inspired by Saskia Sassen's book on the global city because she had covered everything except for health, so I thought I could make a modest contribution. The first question of course that emerges is how the hell do you make a comparison?  We compare, if you look at the UN data, New York, which is 25 million people defined as a metropolitan region, that's a great surprise for the mayor of New York which has eight million people. So we don't even agree on how many people live in different places and how we define the region. We don't even agree on the definition of the region from the US Census Bureau, there are many different definitions. Internationally, everybody's comparing different things and it depends on what you look at. If you're comparing the environment, you probably need to look at the metropolitan region. If you're comparing health services, you need to disaggregate. So what you compare is extremely important. Now we started out 10 years ago looking at this and we found that across these four cities which is where the project began, we could distinguish something we call the urban core by convenience because Paris inside the walls is the little red block. We could compare that to Manhattan, although New York City is often compared to Paris, wrongly. We could compare that to Inner London and we couldn't compare it to all of Tokyo. Of course, you'll tell me on what basis did you say you could or couldn't compare? And the basis was several criteria which I can't go into in depth now but first population size, second population density, third commuting patterns or what the Japanese call the daytime population because these urban cores have many, many more people during the day than at night when they go home and that has repercussions for healthcare and then sort of the historic image of what the city was and a number of other criteria led us to very imperfectly start with this kind of comparison but at least we were clear on what we were comparing. Perhaps it could be critiqued, of course, it's not perfect but it was a way to begin looking at this. And when we looked and again, we started with these cities because even these cities did not have all the data we needed and Tokyo didn't have the data so we ended up narrowing it to three. Now we're trying to extend it to Hong Kong, possibly Shanghai, we'll see. But the richest cities tend, although it's not a general, not often the true, have the best data often. So we started to, and also these cities as Sasuke and others in the field have argued have dominant influence over the world in some sense. And so we thought if we could say something about the healthcare in these cities that was useful, it might have more influence our two minutes. Well, so these were the units that we looked at and we did a number of studies and I'm just gonna give you a very quick flavor. We looked at infant mortality and right here you can see the gap in Manhattan from 18.3 to 3.9, which is stunning. So that has implications for how to design policies to intervene. You take the policy, you intervene where the population is at highest risk and it's not always the same. In Paris there are no gaps of that kind. This is looking at infant mortality over a 10 year period. Not at all, that doesn't mean there are no highest risk areas in Paris but in terms of infant mortality the problem was largely solved although it may be coming back. We did box plots across this and you see that the variation in Manhattan was so much greater than the variation in Tokyo. That's the distribution of the mortality across the different units. We looked at it between the earlier period, 88 and 92 and 93 and 97 and we saw the impact of globalization, a kind of a Manhattanization of the world although Manhattan remains the most socially segregated and the one with the most glaring disparities. The real question to do a little advertisement for myself, I apologize. In this book on the cities where you could find these data how do you judge a healthcare system? And I would say never on life expectancy at birth. Certainly on the probability of survival from specific diseases, although we have no data on that. Possibly on premature mortality. We selected three criteria to start. It's better than 67 which the Commonwealth Fund uses in looking at performance. I think it's better than the WHO for criteria on healthcare performance. Those justifications for these are found in the book. We looked at avoidable mortality which means the kinds of mortality that can be avoided if the health system is working properly. Two, we looked at access to primary care because primary care we discussed is rather important and if you don't have access to it then your conditions get worse and you end up in the hospital. And one has to think of healthcare organization and the conception of that. Most people agree as putting a lot of emphasis on primary care if the goal is population health. And then we looked at access to specialty care. Since I have no more time, I cannot show you my conclusions but I wanted at least to show you the methods and we could discuss. Thank you Ricky. The definition of avoidable mortality is rather important. It's a well-developed concept. It's been developed in Netherlands and London. London School of Economics Martin McKee has been on and Mackenback in Netherlands. It's death before 75 from causes which you should not die from if the medical care system is operating properly. Eschemic heart disease, malignancies of breast, colon, cervix and skin, tuberculosis, maternal deaths. You look at how many people have died from that. And we compared this for Manhattan, Paris, Inner London and Hong Kong and Hong Kong was almost the best but Paris was even better than Hong Kong. I think that that is an indictment of systems that have high rates and Inner London does much worse than Greater London as a whole of course and worse than all of England. But that reflects some of the problems that you were hearing about in Inner London. More to discuss on that later. We looked at avoidable mortality as a percent of all mortality in the four world cities and in that respect Hong Kong has the highest percent of avoidable mortality which is further corroborating evidence and I must say this study was done in conjunction Jean Wu and Patsy Chao who is here somewhere if you should, it has more detail we're trying to publish that. No that's been published, this is all published but we're working on the other one to publish. The other examples and I think I'll stop there are all systems go out in public and they say that they have good primary care. I've never heard anyone say that a system doesn't have good primary care except in New York where you cannot make a defensible case. But the problem is I don't believe in evaluating primary care based on all these criteria of quote unquote good primary care. I'm a consequentialist. I look at what happens if people don't get primary care. That's simply and I don't care whether they do what they're supposed to do or not. I look at what happens if the primary care system is not working and if you are admitted to the hospital for bacterial pneumonia, for congestive heart failure, for asthma or for the complications of diabetes then you're not receiving good primary care and that means you're suffering more pain and the system is suffering too much hospitalization and most people concur that that should not be the way it is. At those conditions are very different than marker conditions but I won't get into that. But the results here and I end are that across Manhattan, Paris and inner London marker conditions are about the same but Manhattan has the worst access, the highest levels of avoidable hospitalization followed by inner London followed by Paris. Why is another question for discussion? And when we bring in Hong Kong into the discussion Hong Kong is very high, it's not as bad as New York City and this is for people over 65 but it's very high considering how healthy most Hong Kongers are and it indicates in conjunction with the studies we did on and the key points I've been asked to conclude with are first that Hong Kong does have better population health that would seem than other world cities but it's very difficult to disentangle the role of the healthcare system from city and population characteristics and other social and environmental characteristics. We do need more comparative research, I've said that among cities and their neighborhoods which examines alternative strategies to protect and promote population health and to deliver healthcare services. I would integrate them, not separate them. And a noteworthy convergent trend in New York City, London and Paris is the increasing recognition. It's not always implemented but it's recognized that the neighborhood is a critical unit for interventions targeted to improving the health of populations at the highest risk and I think this has come through in some of our earlier discussions. Thank you for tolerating my voice and I sincerely apologize.


Recorded books and Arby Digital present, Someone to Honor. A Westcott novel by Mary Ballog, narrated by Rosalyn Landau. Chapter One. Home at Last. Well, back in England, at least. Twenty months had passed since his last brief disastrous stay here after the Battle of Waterloo in 1815. Now he was back. But as Lieutenant Colonel Gilbert Bennington, guilty to his friends and acquaintances, disembarked from the packet in Dover after making the night crossing from Calais, he felt only weariness, irritation, and a heavy foreboding that coming home was not going to bring happily ever after with it. He grimaced at the sight of an elegant traveling carriage. Ducal crests and... Sample complete. Ready to continue?