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135 CIaire Broome "introduction by Session Chair" Transcript of Presentation Summary of Presentation Video Presentation Dr. Claire Broome serves as the senior advisor to the director for integrated health information systems at the Centers for Disease Control and Prevention (CDC). Dr. Broome oversees the development and implementation of CDC's National Electronic Disease Surveillance System, one of the highest priorities of CDC and the administration. Dr. Broome served as deputy director of the CDC and deputy administrator of the Agency for Toxic Substances and Disease Registry (ATSDR) from 1994 to 1999; as CDC's associate director for science from 1990 to 1994; and as chief of the Special Pathogens Branch in the National Center for Infectious Diseases from 1981 to 1990. Her research interests include epidemiology of meningitis and pneumonia; meningococcal, pneumococcal, and Haemophilus b vaccines; observational methods for vaccine evaluation; and public health surveillance methodology. Dr. Broome has received many professional awards, including the PHS Distinguished Service Medal, the Surgeon General's Medallion, the Infectious Disease Society of America's Squibb Award for Excellence of Achievement in Infectious Diseases, and the John Snow Award from the American Public Health Association. She was elected to membership in the Institute of Medicine in 1996. She graduated magna cum laude from Harvard University and received her M.D. from Harvard Medical School. She trained in internal medicine at the University of California, San Francisco, and in infectious diseases at Massachusetts General Hospital. 135

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136 DR. BROOME: Good afternoon. Let me just go ahead and get started with the afternoon's first session. I am Claire Broome, medical epidemiologist at the Centers for Disease Control and Prevention where I have been involved in actually getting the data that you all would like to have as part of the targets for your modeling and simulations, and I have worked closely with a lot of the folks working on bioterrorism preparedness, and I will be moderating this session and what we will do is go through the first presentations and then try to pull some of this together and have a more interactive session during the discussant time period. We hope there will be some time for questions to the presenters as we go along but that will depend on how much the presenters keep to time. So, the first presenter is Ken Kleinman from the Harvard Medical School, and he will be talking ambulatory anthrax surveillance, an implemented system. 136

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137 Introduction by Session Chair Claire Broome Dr. Broome introclucect herself as a mectica] epictemio~ogist at the Centers for Disease Control and Prevention and saint that she is invo~vect in obtaining a good amount of the data used in bioterrorism moclels and simulations. She is also involved with scientists who are working on bioterrorism preparedness. 137

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138 Kenneth Kleinman "Ambulatory Anthrax Surveillance: An implemented System, with Comments on Current Outstanding Needs" Transcript of Presentation Summary of Presentation Power Point Slides Video Presentation Kenneth Kleinman is assistant professor in the Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care. He serves as the main biostatistician on three CDC-funded projects to implement surveillance of health care system utilization in the Boston area and nationally, and he works with the national BioSense project. His interests include the analysis of longitudinal and other clustered data, epidemiologic methods, and missing data problems. 138

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139 DR. KLEINMAN: Thank you. After the generality of this morning's talk I feel like this subtitle here is kind of grandiose, but I am a biostatistician and I am going to actually going to show you real data and describe a system that is running now in Boston, and there are a lot of people involved with getting the system running. It is actually a collaboration between the academic department, an HMO care provider and the State Department of Public Health. So, all these folks are involved in various aspects from those places. My two co-authors on the statistical part of what I am going to talk about today are Ralph Plasers and Rich Plott who are infectious disease epidemiologists. So, here is the outline for the talk. I am going to talk about why surveillance is important especially for anthrax and then I am going to talk about the data that we have and where we are, and I have tried to organize the talk around problems and our approaches to those problems that we encounter while trying to set up the surveillance system and where we should go in the future. So, why is surveillance for anthrax important? Anthrax is what they call a biphasic disease and what happens is you get exposed. You have no symptoms for a 139

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140 while. Then you have symptoms that are very non-specific and they resemble a cold or flu and that happens within a couple of days and actually we don't know a whole lot about anthrax because there have only been about 40 cases in the United States this century including last October in humans I should say. So, there is not a whole lot known about it but it is supposed that most people have symptoms within a couple of days and then a day or two after you have those symptoms you start having really severe symptoms like severe sweating and breathing problems and eventually shock, and if you are not treated then there is death in 98 percent of cases and there is even death in some cases where there is treatment. So, what are you going to do if you have anthrax? Well, nothing when you get exposed and when you start having symptoms you might go see your doctor and if you don't go see your doctor then or if you don't get diagnosed correctly then when you start having the more severe symptoms in the second phase you probably go to the hospital and get ciprofloxacin or another approved treatment. So, there are a bunch of surveillance systems that are up and running now in the country that are based 140

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141 on hospital surveillance, and what they do is they wait around in the hospital and they see if there are too many emergency room visits or too many diagnoses of anthrax and what can they do if they detect anthrax? Well, there are lots of people already in Phase II of the illness, and they are very sick and they are going to be arriving in the hospital in large numbers. So, at least you can be ready for them to come. So, you introduce some good if you do surveillance on that basis, but it would be better if we could detect people when they visit their doctor instead because that would be the earliest time that we would know about it, and also then we could just break out the drugs and prevent people from even entering Phase II and probably save them a lot of discomfort and problems and even lives. So, our data, our study, as I mentioned we are trying to do this. Our position is located between a health maintenance organization and a provider group and also we have, we are in an academic department. I am in an academic department. So, the people who are part of this group, this care group and HMO, there are about 250,000 people in a certain area of Massachusetts, and that is about 10 percent of the population in that area. So, what is very nice about this is that we can actually attempt to do that surveillance on the doctor's 141

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142 visit before people get to the hospital because the provider group uses ambulatory medical records data and what that means is that every time you go to your doctor's office they actually have a PC in each examining room, and they will type in information about you including an ICD-9 diagnosis, and that is just some coding system for a diagnosis if you are not familiar with it, but it is symptoms and confirmed diagnosis, and they are continuously updated. They are centrally stored by the provider group, and we don't have to do anything different from what they are already doing, and it is standard practice to record all the diagnoses they make for each person who comes to see them,and that includes phone calls and nurse practitioners and physicians, and the system is actually a commercial system. So, it is relatively easy for us to take our system which is in Eastern Massachusetts and transport it to some other locale where they happen to use the same medical records system and there are a bunch of them. PARTICIPANT: Does this system store all the medical record? DR. KLEINMAN: It stores all the medical record. Things like pharmacy data and test results aren't always updated in the same system. So, it is all the direct 142

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143 patient contact between the physician and the patient. Does that answer your question? PARTICIPANT: Lab results? DR. KLEINMAN: Lab results are not -- PARTICIPANT: No, would have been a good answer to my question. DR. KLEINMAN: Okay, I guess I wasn't clear enough on you question. Now, I am going to start talking about the problems we encountered when trying to set up the surveillance, and the first one is that it actually uses adult standard test for anthrax, and you have to get a chest x-ray, and I don't know enough about medicine to know what about this x-ray says that there is anthrax here, but if we actually waited for chests x-rays we wouldn't do any better than hospital surveillance because the physicians don't order chest x-rays for people who come in with cold symptoms. So, we can't actually do surveillance for diagnosis of anthrax, and so what we do instead is we define symptom clusters or syndromes, and what we do is we say that anything that your doctor might say if you came in with the first phase of anthrax problems we are going to try to collect that, and we call that lower respiratory illness or I might say lower respiratory infection later 143

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144 because those are the symptoms that characterize the first phase of anthrax infections. That includes, I listed cough, pneumonia and bronchitis because about 90 percent of the diagnosis falls in one of those categories in this syndrome which includes about, I think it wrote it down here, 119 ICD counts go in there and that syndrome we actually borrowed from a Department of Defense product that was doing this before we were in a slightly different context. So, in our data set in about a 4-year period there are about 120,000 visits that found this syndrome, and if you think about a natural disease unlike anthrax if it doesn't get fixed the first time you go to your doctor you are going to go back again, and when we looked at our records very nearly about one-third of them were repeat visits that were shortly after other visits. So, based on the clinical expertise of the MDs working with us we were able to say that if it was within 6 weeks of a previous visit, the previous visit we weren't interested in it. It was probably the same illness, and so we throw those away, and what is left we call episodes of lower respiratory infection. PARTICIPANT: How many don't visit with the same illness? 144

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145 DR. KLEINMAN: I have no idea. This is a very small portion of the doctor's visits. I don't know that information. It is a completely different order of magnitude. I don't know the answer. So, here is actual data and each dot here represents the count of visits for their respiratory infection on a given day in between January I, 1996 and December al, 1999 or January I, 2000, and I just want to point out a couple of interesting features here that will come into play later, and the first is that you can see that during the winter there are big peaks. People tend to go to their doctors for respiratory complaints more in the winter than they do in the summer presumably because they are more likely to have those symptoms and the other interesting feature is that you can see there are two trends here, and our clinics are actually open on the weekends. So, this lower band is the weekend visits and the upper band is the week day visits, and finally, I don't know how clear this is from here but you can see these points, really low here in winter. That is Christmas and New Yearls. So, people don't go to the doctor on holidays but they will go as often on the weekends, and they tend to go an awful lot more in the winter than they do in summer. 145

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250 goal of this workshop is to get exactly at that question is that, you know, these are important problems and, you know, this morning we heard in the data mining session that there is a lot of proprietary work that is going on clearly and for good reason and things that are going on in different sectors and how do we share, but if we really want to energize the mathematical sciences community to try to help address the homeland security problems and vice-versa, we have to begin to address exactly what Tom said. How do we do this quickly? You know, how do we find and mobilize the people to try to do these problems and to pull them away from their really successful research? So, anyway, so hopefully, during the course of this workshop some good ideas will come out. MS. BROOME: I would like to suggest that the way the research -- the workshop is set up has the potential in terms of you brought in folks like me, who are not mathematicians, but I actually spend a lot of time thinking about what do we need to have an effective surveillance and response infrastructure. But I must say this is only going to work if we, I think, think about cross disciplinary teams but think about what are the most likely questions or challenges to set them where there is likely to be a payoff. I will pick 250

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251 one where I tend to doubt it, the suggestion that maybe we do inverse modeling to find out the source of an outbreak. You know, to an epidemiologist, that is an intriguing thought, but I would much rather send out a team of epidemiologists to interview a bunch of people and find out what is going on. We do that and we generally find the answer. I am not saying that -- PARTICIPANT : [Comment off microphone.] MS . BROOME: Talk to the FBI . I don't think modeling is going to do that one either. On the other hand, I have heard some -- you know, there is clearly a whole area around detection that Ken is addressing, but indicate a much broader range laid out, which are going to alone as was alluded to in the data mining session, aberration, detection issues there is, as I was trying to of possible syndromes, as Art have different challenges, let you know, you have got to have the data to apply these to. So, there is some highly applied questions in having data available electronically so that these wonderful tools can be used. We are sort of working on that side by trying to get down to the nitty-gritty with clinical information technology systems to say what data can we get tomorrow 251

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252 that then might be used for these kinds of -- to validate how useful these different approaches might be. I think, obviously, Sally has given a very concrete example of how policy decisions on vaccine usage could be approached with modeling. There is a very active debate on the use of small pox on the vaccinia vaccine or other new vaccines that benefit from that kind of a quantitative approach. So, those are just some things I would throw out as focus areas that are -- you know, would benefit from -- MR. TONDEUR: I want to return to the idea of the infrastructure for this and I want to say two avenues within the Division of Mathematical Science. One is focused research groups, which are specifically to address such issues and the other is an institute we plan to fund attached to the American Institute for Mathematics, which is specifically targeted to have group focused workshops, which actually do the work, where you can assemble teams from different disciplines. MS. CHAYES: I have got one more area, which kind of came up, I think, in Simon's talk, which is games theory. What I have seen happening in network research is that for awhile people were just looking at the structure of networks like the Internet or the worldwide web and now 252

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253 they are overlaying game theory on that. So, some cost benefit analysis or some protocol analysis on top of that and if you want to try to implement some of the things that Sally is talking about, you might want, rather than just looking at the differential equations, to take one of Steve's networks and then, you know, put on a game theory functional that would give you some cost benefit analysis on top of that and see how that -- what the results of that are. I think that that is an area of mathematics that people are just starting to look at for networks for the Internet and the worldwide web and it would probably also be very useful in this context. MR. LEVIN: The fact that the networks are in some sense adaptive, that as you make interventions, the networks will change in some of the directions. So, for example, if you remove a focal vertex that in the case of a sexually transmitted disease or prostitute another note becomes crucial. MR . TONDEUR: When you say games theory, I sort of -- if I were a terrorist, I would do the following game theoretic approach. I would say how would I achieve the maximum damage. But then that means we should probably play that same game and say, okay, so if I were a 253

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254 terrorist, how would I achieve the maximum damage, then how can we protect against. PARTICIPANT: They don't always think that way. Remember, the main thing, how do they achieve the maximum terror, not the maximum damage. There is a difference between those. PARTICIPANT: That could be a measure of damage. MS. CHAYES: It is a different function. MR. LEVIN: I just don't want people to think about this problem in just the way, you know, how many people are going to die. That is not the only way -- MS. CHAYES: Right. Well, the anthrax certainly didn't kill a lot of people, but it terrorized people, but that is just a different -- I mean, if you want to model, that is a different functional for your game theoretic. MR. LEVIN: That is right, but the point I was making is that for either side, the point would be how do I achieve the maximum damage or terror given that the response is likely to be this. Do I assume that -- or how do I, if I am interested in response system, create a response system based on the fact that the terrorists are going to respond to my response system. So, those are the -- what makes it a game theoretic problem. 254

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255 DR. BLOWER: I think the thing -- if somebody said that they had released small pox and alerted all the news media that Washington and New York and San Francisco had now been contaminated and the cases were going to -- that could bluff the government into doing a mass vaccination campaign that would do more harm than good. So, they wouldn't actually have to do anything, just alert the media and the response could be worse than -- I think this needs to be thought about. MR. MC CURLEY: I would like to ask one other question on the detection, only speaking about detection at the level of people getting sick. There are more automated systems that are looking at more pathogenic agents. We can feed into the data mining thing. I don't know how practical it is. My understanding was at the Olympics that we did have some sort of detectors going for anthrax and other things. MR. is doing that MS. KLEINMAN: I know the Department of Defense sort of thing. They have sniffing machines. BROOME: I think, again, it comes back to the specificity and the predictive value of a positive. I mean, just during the Gulf War, there were enumerable alerts of gas that worked as false positives. You know, I think there are some fundamental parameters defining the 255

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256 characteristics of these tools tremendous attention to. MR. MC CURLEY: -- way of merging the first session on data mining -- get rid of these delays. MS. BROOME: There is a lot of interest in that and I think a lot of research ongoing, but it is actually a real challenge. I mean, in many of these settings, as has been noted before, you basically have to have a specificity of a hundred percent to have a useful tool. MR. EUBANK: If I could make a comment about the practicalities of getting mathematicians involved in these research areas, we have a curiosity-driven research model. So, that means that my goal is trying to convince mathematicians that the problems we have are interesting for them to work and that they should be curious about them. But if there is some way to drive research based on our problems, I think it would be worth exploring because it is not -- I think the problems are interesting, but I don't always get agreement from the people I am trying to convince. MS. BROOME: Okay. We are over time. I am looking at folks in terms of -- take a couple more questions? Need to break? Quickly. that we have to pay 256

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257 MR. MC CURLEY: I wanted to ask how many people here have a degree in mathematics? DR. BLOWER: Mine is in biology. MR. MC CURLEY: So, this is a question that you are really -- how do you get mathematicians to interact. This is actually very rare for mathematicians to interact this way with other scientists. MR. TONDEUR: [Comment off microphone.] [Multiple discussions MR. AGRAWAL: MS. CHAYES: the mathematics you want MR. AGRAWAL: MS. CHAYES: MR. AGRAWAL: MS. CHAYES: PARTICIPANT: MR. AGRAWAL: [Comment off microphone.] Those are the things you -- that is ; to look at. [Comment off microphone.] Collaboration of whom? [Comment off microphone.] Surveillance. [Comment off microphone.] [Comment off microphone.] MS. BROOME: I think one of the issues that Art and I could spend a lot of time talking about is the complexities and the varieties of surveillance. For traditional public health surveillance because it includes individual identities, it doesn't lend itself to wide open, although certainly the project that I am managing actually 257

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258 is just getting us on the web for doing traditional surveillance and interfacing with hospitals. But there are also other applications where we do, for example, we are looking at doing kind of survey stuff we do over the Internet so that there is group participation. Then there is some fairly substantial data sets that are put together, for example, of pharmacy data that is de-identified. But most of these are -- you know, they rely on a number of collaborators. I don't think it is the sort of mass computing platform you are thinking about. MR. AGRAWAL: [Comment off microphone.] MS. BROOME: We have thought about trying to -- for example, for our web site, trying to also have two-way communication, where we can record incoming information from physicians or the public. So, you know, I think there is a lot of interest in seeing what could be done with that. But, again, there is complexities when you get down to individually identifiable data that we have to be very conscious of. MS. CHAYES: Do you want the ten minute break now? MS. BROOME: Okay. 258

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259 MS. CHAYES is giving a Also, one other thing, everyone who presentation, please give us a transparencies or your power point presentations 259 copy of your .

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260 Remarks on Detection and Epidemiology of Bioterrorist Attacks Arthur ReingoIc! Disease surveillance is a systematic ongoing collection, analysis, and dissemination of health data, with findings Iinkect to actions in the ctecision-making process. With regard to bioterrorism, Dr. ReingoIct and his colleagues wouIct like to have a system in place to permit them to ctetect illnesses quickly and then be in a position to respond rapidly to keep morbidity and mortality to a minimum. While the 2001 anthrax attacks generated a lot of concern about detection and rapist response to outbreaks, the pub~ic-heaith community comes to bioterrorism ctetection and response with a gooct clear of relevant experience. Generally, it detects outbreaks because an astute patient or family member notices them: "Gee, aren't a lot of us who ate at the church supper all vomiting at more or less the same time?" Similarly, the community learns a lot about outbreaks from astute clinicians who recognize they're seeing more cases of an illness than they shouIct be seeing and are smart enough to let the health department know about that. However, public health workers rarely ctetect outbreaks. Therefore, the goad is to see if they can be a larger part of this detection process, as opposed to waiting for clinicians and patients to tell them about the problem. People are working in various ways to improve surveillance for the ctetection of bioterror events. One approach is to monitor visits to health-care providers, particularly outpatient visits, emergency-ctepartment visits, ciinicaI-microbio~ogy laboratories, and indicators such as 91~ calls, over-the-counter drug sales, and absenteeism at work and at school. Some people are even considering direct monitoring of samples of the population through Nielsen rating-type setups, in which a large group of inclivicluais is routinely answering questionnaires over the Internet. Three criteria will help us judge a proposed system: Sensitivity. Of all the outbreaks that occur, what proportion of them will be predicted by this system? Specificity. Of all the times there is not an outbreak, what proportion of the time will the system tell us there is not an outbreak? The predictive value of a positive. Of all the times the system tells us there is an outbreak what proportion of the time is it right? 260