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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.
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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.
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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.
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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.
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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
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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
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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
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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
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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
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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?
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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.
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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
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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
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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
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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
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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.
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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
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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
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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
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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.
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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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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
Representative terms from entire chapter:
sensitivity analysis
