[I]{.underline} [N]{.underline} [D]{.underline} [E]{.underline}
[X]{.underline}

**USE OF ANTIMICROBIAL DRUGS IN FOOD ANIMALS**

**AND THE ESTABLISHMENT OF REGULATORY THRESHOLDS**

**ON ANTIMICROBIAL RESISTANCE**

January 24, 2001

**[Session 4: Use of Risk Model to Establish Resistance]{.underline}**

**[Thresholds]{.underline}**

Moderator: Dr. Claire M. Lathers

***Use of Risk Model to Calculate Resistance Thresholds***

Ms. Mary J. Bartholomew

FDA/CVM 5

***Use of Resistance Thresholds to Manage Risk***

Dr. Linda Tollefson

FDA/CVM 18

***Comments on CVM Approach: Risk Assessor Perspective***

Dr. Kimberly Thompson

Center for Risk Analysis 24

***Comments on CVM Approach: Microbiologist Perspective***

Dr. Daniel F. Sahm

MRL Pharmaceutical Services 41

***Strengths and Limitations of Threshold Approach***

Dr. Robert Tauxe

CDC/FBD Branch 55

***Effect on Resistance Following Changes in***

***Antimicrobial Use***

Dr. Marc Lipsitch

Harvard School of Public Health 71

***Panel Discussion on Resistance Thresholds*** 79

***Public Comment Period*** 134

**[Session 5: Alternative Approaches to the CVM Proposal]{.underline}**

Moderator: Dr. Bert Mitchell

***Alternatives to Establishing Thresholds***

Dr. Richard A. Carnevale

Animal Health Institute 171

[I N D E X]{.underline} (Contd.)

***Alternatives to Establishing Thresholds***

Dr. Dave Smith

University of Maryland 182

***Alternatives to Establishing Thresholds***

Dr. Tony Cox

Consultant 193

***Public Comment Period***: ***Suggested Alternatives***

***to Presented CVM Approach*** 213

***Closing Comments: Summary, Concluding Remarks***

***and Next Steps***

Dr. Stephen Sundlof

FDA/CVM 239

KEYNOTE: \"\-\--\" denotes inaudible in the transcript.

[M O R N I N G S E S S I O N]{.underline}

(8:41 a.m.)

**[SESSION 4: USE OF RISK MODEL TO ESTABLISH]{.underline}**

**[RESISTANCE THRESHOLDS]{.underline}**

MODERATOR LATHERS: Good morning. Would you please take your seats so we
can begin our next session. Good morning. Would you please take your
seats? Good morning. I am Claire Lathers, Director of the Office of New
Animal Drug Evaluation at the Center for Veterinary Medicine at the FDA.
Welcome to our third day of CVMs public meeting on the Use of
Antimicrobial Drugs in Food Animals and the Establishment of Regulatory
Thresholds on Antimicrobial Resistance.

The meeting so far has discussed CVMs approaches in terms of threshold
establishment in overview presented by Dr. Sundlof on Monday. We on
Monday also discussed antimicrobial use in food animals in a session
moderated by Dr. Andy Beaulieu. Yesterday we addressed human health
threshold establishment in a session moderated by Linda Tollefson. Today
we are going to discuss the use of the CVM risk model to establish
resistance thresholds and alternative approaches to the CVM proposal.

Antimicrobial new animal drug review involves multiple disciplines. It
requires an understanding of how antimicrobials are used in both animals
and in humans. Antimicrobial new animal drug review involves the
interaction in issues related to topics of epidemiology, microbiology,
and risk assessment. Discussions during this mornings session will
focus on all of these disciplines and their interactions. So welcome to
Session 4, the Use of the Risk Model to Establish Resistance Thresholds.

***[Use of Risk Model to Calculate Resistance Thresholds]{.underline}***

***By Ms. Mary Bartholomew***

MODERATOR LATHERS: Our first speaker this morning will be Mary
Bartholomew. Mary has an MS degree in statistics from Temple University.
She is currently finishing her Ph.D. in biostatistics at the Medical
College of Virginia. Mary has been a member of CVM since 1990 and has
coauthored over 40 published papers. Since 1997, Mary has concentrated
on statistical issues related to the preapproval aspects of
antimicrobial drug resistance. Today Mary will discuss the use of the
risk model to calculate resistance thresholds. Mary.

MS. BARTHOLOMEW: Thank you, Dr. Lathers. Good morning. As Dr. Lathers
just said, I am presenting a method for calculating the resistance
thresholds. Its a method that is risk model based. The resistance
threshold tx, little tx, is the maximum allowable prevalence of
resistant bacteria isolated from animal-derived food that does not pose
an unacceptable human health impact. The significance of the resistance
threshold is that prevalence of resistant bacteria isolated from
animal-derived food will be monitored and compared to the resistance
threshold. Its assumed that when the resistance threshold is exceeded
the human health threshold is also exceeded and regulatory action is
required. Dr. Tollefson will be discussing this aspect further.

(Slide.)

A risk-based approach to establishing resistance thresholds is based on
a proportional relationship between a number of people affected and a
quantity of meat with resistant bacteria. This contaminated meat is the
exposure in this case, and were talking about the quantity of meat to
which the population is exposed. Were not talking about individual
exposures in this instance, and were talking about the relationship of
that exposure to an undesirable outcome. In this case the human cases of
illness caused by bacteria resistant to a drug of concern with which
they will be treated, and that resistance is attributed to the use of an
antimicrobial in the animal.

The relationship is expressed through the proportionality K-res value,
which relates the nominal mean number of cases of illness due to drug
resistant bacteria attributable to a particular food product derived
from the given food animal species to the quantity of food animal
product consumed that contains drug resistant bacteria.

Now, I was originally planning to have a fairly short talk and have time
for questions, but yesterday in comment period we had a request for some
further explanation about K-res, and I would like to take a minute or
two to try to explain it a little bit further than what I originally
planned to. The question about K-res is how does it incorporate a number
of other factors \-- that was the question that was asked yesterday.

As I said, were talking about the quantity of meat that contains
bacteria, some of which is resistant. Were talking about a quantity
which is measured in a consistent fashion, such as through the NARMS
program or whatever were going to set up in terms of slaughterhouse or
retail assessment of resistance. This is the total quantity to which the
population is exposed. So once this meat leaves the slaughterhouse or,
if its measured at retail, leaves the grocery store, then people will
do what they will do with the meat. It will be either cooked or not
cooked properly. It will cause cross-contamination in the kitchen, and
people will clean up the kitchen appropriately or not. It will be served
in restaurants. Whatever happens to it happens, and that is sort of the
national pattern of usage.

That pattern of usage is what links the quantity of meat that we
observed with the number of cases of illness that are due to resistant
bacteria that we also will be observing. The K absorbs all these various
things that are happening and tells us what the association is. A
certain quantity of meat yields a certain number of
resistant-attributable cases of disease.

So the population is also somewhat representative of whatever it is. It
contains a certain number of immuno-compromised people, and if those
people are the ones who are more susceptible to having these illnesses,
they are the ones who are going to show up predominantly in the number
of cases that we observe. All of this is sort of observed on a
population average approach, and we hope to monitor in such a way that
we will pick up seasonal trends since we will monitor from year to year
somewhat consistently. So that if there are seasonal trends, it will be,
year to year, taken into account.

So then Q is that quantity we expect to see. There was some question
that has been raised about the unites of Q. There are some interesting
academic pathologic examples that you could talk about such as having
millions and millions of pounds of meat and all the resistance
concentrated in just a few of those pounds. Thats not what were
talking about. Were talking about what were likely to see and what we
think that our sampling scheme is representative of picking up. H will
be sporadic cases of illness. That is what FoodNet is keeping track of
and hopefully will not be influenced by an outbreak.

(Slide.)

So going back to what I intended to say, a measurable human health
impact, thats what were talking about, is the current value H(x),
which is the current measurable level of human health impact resulting
from the use of an antimicrobial drug in food-producing animals causing
resistance to an antimicrobial drug used in human medicine. In an
earlier session youve already heard Dr. Flynn discuss human health
thresholds. Whether it is a measurable level of human health impact
resulting from use of an antimicrobial in drug-producing animals causing
resistance to an antimicrobial drug used in human medicine or a
calculated maximum, we will designate the impact as capital H(x). This
is opposed to the human health threshold of unacceptable human health
impact designated capital T(x) yesterday in Dr. Flynns talk, where
again x stands for the impact of concern such as if we are going to
have thresholds for enteric illness or systemic illness.

(Slide.)

The proportional relationship previously defined can be re-expressed as
a definition of K. K-res can be written H(x)/Q. That is the human health
impact divided by the quantity that was the exposure resulting in that
H(x). Now since the human health impact threshold, capital T(x), is a
particular value of an observable human health impact, there is a
particular value of Q or quantity of exposure for which the same
relationship will hold. That is K-res is the human health impact
threshold divided by that quantity Q~T~ which is associated with T(x).

(Slide.)

So I have already mentioned how Q is this population averaged value of
meat that were exposed to, and we actually dont have a directly
measurable quantity of it. We estimate it as illustrated as the product
of the total pounds of product consumed \-- Sorry. I got confused,
because there is a product that means a commodity and a product that
means multiplication and the the proportion of sampled pounds containing
bacteria and the proportion of samples from which resistant bacteria are
isolated, which is little h. So assuming that total pounds consumed
and the proportion of sampled pounds containing bacteria stayed constant
for a short period of time, one can see that Q and Q~T~ would differ
only in the third multiplicand, proportion of samples from which
bacteria are isolated.

(Slide.)

So in defining Q and Q~T~ then, the proportion of samples from which
resistant bacteria are isolated is called h in defining Q, as we just
saw in the previous slide, and called little t~x~ in defining Q~t~(x),
which is the resistance threshold.

(Slide.)

Again, we will talk about the little t(x) as the maximum allowable
prevalence of resistant bacteria isolated from animal-derived food that
does not cause an unacceptable impact on illness in humans.

(Slide.)

We anticipate that from one year to the next year consumption patterns
in the United States, rates of carriage of bacteria on the product and
rates of human prescriptions for the drug of interest will not vary
vastly. Thats from one year, the one we measured, to the next.

(Slide.)

So then during this stable period we could set these two quantities
equal, the two definitions of K-res. H, an observable human health
impact and its associated quantity of exposure H(x)/Q, and the human
health threshold and the quantity of meat that represents the exposure
associated with the threshold T(x)/Q~T~. Further, because the volume of
consumption and the carriage rates of bacteria which appear in both Q
and Q~T~ will be the same, there will be cancellation. So the
cancellation leaves only h in the quantity where K-res is defined in
terms of a current observable health impact and an observable current
resistance, and the threshold level that, as we have said before, is
going to be set by policy, and the resistance threshold that would
represent that maximum allowable level of resistance.

(Slide.)

Because they are equal we can then set one equal to the other and this
allows for a solution for the threshold level of resistance. You see
that the solution is capital T(x), the human health impact, times an
observable level of resistance, divided by the associated observable
human health impact.

(Slide.)

However, during the approval process for a new animal drug, information
on a current human health impact due to the use of that drug and the
resistance in the animal isolates due to that drug will not be
available. So one solution for surmounting this problem is to use a
hypothetical construct as indicated. We call it the maximum impact
method. Without current information we use the max. Assume all isolates
from the food commodity are resistant. Assume all human cases
attributable to the food commodity are caused by resistant bacteria. In
fact, if all of the isolates from the food animal commodity are
resistant then it follows that all cases attributable to the food
commodity that are resistant would be resistant. Then making those
assumptions, h=1, and H~max~ would be all attributable cases.

(Slide.)

The following graph illustrates that when the line is drawn through
H~max~ at 100 percent resistance and through (0,0) you will have a line
that will pass through all values, and therefore the human health impact
threshold must be somewhere along that line. Somewhere along that line,
wherever it is, we can drop down to the x-axis and then find the
associated prevalence of resistance among the animal isolates that would
be associated with that impact. Here we have shown the maximumat the far
right, and if you were to have a current health impact it would be a
point on the line. Weve shown an example current value. This is current
value. It says little h, but of course this has two coordinates. So its
little h and the current value of human health impact, capital H, too. I
apologize if you cant read this. It is figure four in the document, and
you can take a closer look at it.

So you can see that if we were to use rather than the mean, the 95th
percentile, we would have a more conservative estimate of the resistance
threshold because, of course, the 95th percentile crosses certain levels
sooner than the mean value does. This would add some level of
conservatism. Another way of thinking about it is if we calculated the
resistance threshold, we could also look at the lower 5th percentile.
Say the calculated value was 40, but the lower 5th percentile were more
like 30. Then that would be another way of adding some conservatism to
the determination. That conservatism could allow for compensation for
statistical uncertainty in the measurement of a number of the quantities
that weve discussed as being necessary in this whole process.

(Slide.)

Finally, I would like to talk about what happens if a product is
approved for multiple species of animals. In that case its going to be
necessary to apportion the human health impact among the different
species such that the total impact does not exceed the human health
threshold. One way to do this is to use weights. Weve got a weight a~i~
in the equation. This inequality says that some weight a~i~ times the
threshold for a given species, t~i~, and summing those for all i \-- for
those of you that have had any basic course in statistics, you recognize
that symbol probably \-- should be less then the total human health
impact.

So if we were to use uniformed weights such as 1/4 for each of four
species for which the product were approved, then what would happen is
the species that contributes the most to human disease would have the
lowest resistance threshold. Thats one way of going about it. Other
weights are possible and may be discussed in the panel session after
this and later. Id like to thank you for your attention, and is there
time for questions?

MODERATOR LATHERS: Yes. I think given the importance of your talk we
will entertain a few questions for Mary right now if you have any.

DR. MORRISON: Fred Morrison. There are a lot of assumptions here. Im
interested in some of the assumptions about meat exposure. Is that
including all types of meat?

MS. BARTHOLOMEW: We would do this on a commodity basis. So that as
indicated in the last slide, that we would have a measurement for
exposure for beef, say, a measure of exposure for poultry, for swine,
and possibly others. Im not sure.

DR. MORRISON: Is there some evidence that actually the meat exposure is
what causes disease in these individuals?

MS. BARTHOLOMEW: I think there are a lot of scientists who have
testified during these last two days that it is not meat directly. Its
the animal contact with the meat such as through cross-contamination,
and I think that we heard testimony to that affect.

DR. MORRISON: Right. So theres a lot of other factors, and one of the
factors that Im very intrigued with are some of the other food elements
that people are exposed to. One of them being whey, for instance, which
contains a lot of organisms such as enterococci, this type of thing.

The other thing is the assumption that everything is stable over a
period of time. Have you gone back over a number of years to show thats
the case with meat consumption, production and consumption?

MS. BARTHOLOMEW: We do know that over extended periods of time
consumption patterns change. Theres more movement toward processing \--
processed products or whatever. We have built into this the notion that
since things do change and this is calculated on the basis of assuming
stability that we would go back and we would reevaluate. Secondly, if
K-res is capturing kind of the situation at the current time and then
people were to introduce methods or processing, something that would
change the level of contamination such that you would have fewer cases
of human illness, then it would also be true that you would progress
toward the resistance thresholds more slowly. At least thats what the
assumption is.

DR. MORRISON: Theres also an assumption here that all the disease is
caused by resistant organisms. I dont know the evidence for that also.

MS. BARTHOLOMEW: No. I dont think that were assuming that in terms of
the reality. What we are saying is we assume it for the purposes of
calculating the resistance threshold in the absence of other
information, and since the resistance in the food commodity relates to
the human cases of illness caused by that \-- the resistant cases in the
way that were specifying, then we can look at the max and then go down
that line to a place where weve got the level of resistance that were
talking about. So were not really assuming that all cases are caused by
resistant organisms except for the purposes of that calculation. If we
had any other current value along that line we could use that instead.

DR. MORRISON: Okay. Thank you very much.

MS. BARTHOLOMEW: Youre welcome.

MODERATOR LATHERS: Well entertain one more question only, and then
please, for the rest of you, retain your questions for Mary during the
panel discussion.

DR. KATZPER: Meyer Katzper. CDER. My understanding of resistance is that
bacteria get resistant when they dont respond to antibiotics. If a
large number of people get sick from animal products, it means that the
animal products have bacteria in them. It doesnt mean that the bacteria
were resistant. If the people were then treated with antibiotics and
didnt respond, then we know that they had gotten resistant bacteria. So
that this methodology and this model doesnt capture that at all.

MS. BARTHOLOMEW: Does not capture the human use of the drug?

DR. KATZPER: The resistance of the drug as defined in terms of response
to antimicrobials, which is a question which is very serious, and thats
the question that should be addressed. Because just contaminated meat,
thats a question for the health department. Drug resistance is a
question for FDA. Drug resistance as defined by its response or lack of
response to antibiotic, this model doesnt capture that.

MS. BARTHOLOMEW: Thats true. We mentioned yesterday that what were
talking about is the impact that having a resistant organism might have
and that may lead to non-response to therapy or it \--

DR. KATZPER: Well, but were not capturing that. These are separate
concepts and these are separate things which have to be captured, and
they require different models.

MS. BARTHOLOMEW: I dont know. Im obviously not addressing your
question. Hopefully someone else can do that. We tried to set out in the
threshold document what we think the outcome of interest is, and there
may be some debate. And in fact there has been some debate about that
before yesterday, and I guess its ongoing.

VOICE: I have a statistical question I think you would be able to answer
if weve got time for that.

MODERATOR LATHERS: Im looking at the clock, and I think well save our
questions for the panel discussion if you would, please.

VOICE: Certainly.

MODERATOR LATHERS: Linda, perhaps you could come up.

***[Use of Resistance Thresholds to Manage Risk]{.underline}***

***By Dr. Linda Tollefson***

MODERATOR LATHERS: Our next speaker is Dr. Linda Tollefson. Linda earned
her doctorate in veterinary medicine from the University of Illinois.
She holds a masters in public health from Hopkins. She is presently the
Director

of the Office of Surveillance and Compliance, but as

of February 1 she will be the Deputy Director of CVM. Linda is one of
the founders of NARMS, that is, the National Antimicrobial Resistance
Monitoring System for enteric bacteria. Today she will discuss with us
the use of resistance thresholds to manage risks. Linda.

DR. TOLLEFSON: Thank you, Claire. What Mary presented was how resistance
thresholds can be calculated using an epidemiology-based model thats
relating the prevalence of resistant bacteria on the food to the human
health impact that were going to set up by policy. I would direct you
to look at the more \-- many of the factors that are involved in that.
Go back to the campylobacter risk assessment, which is on the homepage,
and that might address some of the questions I think. Its a little
simplified version.

(Slide.)

I want to discuss how FDA envisions this risk, the resistance threshold
as a risk management tool. To reiterate, the resistance threshold is a
maximum allowable prevalence that does not cause a human health impact.
Its established for a particular antimicrobial use in animals and
correlated to the human health impact threshold. If that resistance
threshold then is exceeded, the drug is no longer shown to be safe in a
given food animal species.

(Slide.)

What that does is it gives a FDA essentially a trigger for initiating
immediate withdrawal from the label of the species that has reached that
resistance threshold. The resistance threshold is measured in animal
products. It will be measured by NARMS, either slaughter plant isolates
of retail food isolates. It cannot be measured by diagnostic lab
samples, which is the other component of NARMS.

Now, 2001, FDA is greatly expanding NARMS. Particularly of relevance
here is expansion into retail meat samples. The slaughter plant isolates
are dependent upon FSIS goals and objectives and so forth in the
programs. Retail meat samples will give us a lot more flexibility in the
type of pathogens we can isolate for and also the species levels.

We will also be taking samples of animal feed, but I think thats a
little bit less relevant, at least at this stage. So we will have
resistance threshold, but that doesnt mean that voluntary action cant
be taken long before that threshold is reached. If decreases in
susceptibility or changes in the prevalence of resistance among the
animal isolates are observed, but the resistance threshold is not exceed
yet, mitigation can be implemented at any point.

(Slide.)

Therefore a major advantage to the resistance threshold is that it is
predetermined and there for everyone to see. Its truly a bright line
not to be crossed. Our goal is to ensure that significant human
antimicrobial therapies are not lost due to the use of the related drug
in animals. NARMS is publicly available surveillance data. So all
interested parties can monitor the levels of resistance or changes of
susceptibility for either individual drugs or relative to other drugs in
the same species for example. If the levels then begin to approach the
resistance threshold, intervention strategies or mitigation can be taken
by the people in control of the drug use. Those may be practicing
veterinarians. It might be animal producers. Distribution of the drug
can be controlled by the drug industry. Trade associations, et cetera,
they can all work for that.

(Slide.)

What it is doing is allowing for self-regulation. Now we have made the
decision not to set preliminary thresholds. You have seen at this
conference that its not going to be that easy to set the human health
impact thresholds. Thats how we calculate the resistance thresholds. We
think it will be even more problematic to step back off that and
determine some level at which were going to dictate a mitigation
strategy or action should be taken. So weve elected not to do that.
Those will be voluntary.

Also we think that the process of just setting the thresholds and
monitoring them is going to be very helpful to reinforce the importance
of the judicious use guidelines. There are other advantages. A
resistance threshold can provide guidance to drug companies if they are
looking to seek an additional approval say for example in an additional
species, or even a new drug in a particular drug class that has been
previously approved. We also think that it will help somewhat in a
related way to look at research thats needed on antimicrobial
resistance simply by highlighting the gaps or the holes.

(Slide.)

I want to spend a minute on judicious use activities, because the AVMA
has been working on this from a number of different fronts, and we think
that they will be very helpful in maximizing the usefulness of
resistance thresholds. There is a steering committee on judicious use.
Theyve completed general guidelines and several species-specific
guidelines. Swine, cattle, and poultry are already developed. Theyve
also won a contract with CVM to develop material about judicious use.
Were going to do booklets. Theres a videotape script and speeches.
That will serve to get the message out to as broad of an audience as
possible. I think we all know what judicious use of antimicrobials are,
but Id like to put those up.

(Slide.)

Monitoring resistance levels against the resistance threshold also
provides a means to gauge the success of the prudent use guidelines over
time. Well be able to see if theyre implemented, what is happening, or
they can guide changes in those guidelines.

Also it will be possible to select drugs for a particular disease
etiology based on current resistance levels relative to those resistance
thresholds. For example, the veterinary antimicrobial decision support
system that was described by Dr. Apley on the first day of this
conference will be very useful as a source of information needed by
veterinarians to make decisions on antimicrobial use.

Thats all I have to say. I would like to put a plug in for NARMS. Were
going to have an annual meeting on March 15th and 16th. Its going to be
held in this same hotel. Theres no registration meeting, but you do
need to preregister. I mean, there is no registration fee, but you need
to preregister, and you can do that on the homepage.

MODERATOR LATHERS: Thank you, Linda.

***[Comments on CVM Approach: Risk Assessor Perspective]{.underline}***

***By Dr. Kimberly Thompson***

MODERATOR LATHERS: Our next speaker is

Dr. Kimberly Thompson. Kim has earned her masters from MIT and her
doctorate in science from the Harvard School of Public Health. She is
currently an assistant professor of risk analysis and decision science
in the Department of Health Policy and Management at the Center for Risk
Analysis at Harvard. Kim will now share with us her comments on the CVM
approach from a risk assessors perspective. Kim.

DR. THOMPSON: Thank you. Its a pleasure to be here and to have a chance
to talk about this topic from a risk analysts perspective, and I think
Ill start out with the question, Well, where are we?

(Slide.)

For those of you who cant read the cartoon from the back, theres a
woman sitting at a lunch counter, and shes looking at the menu; and, in
addition to the prices, she is getting information about the risks and
the benefits of the various choices that she has in front of her. I put
this up here so that we can start to appreciate that we are getting much
more sophisticated in our consumption of foods and our expectation for
food safety, and in how we think about the risks and benefits of the
food that we eat. I think one of the critical things about this meeting
is that it is providing us with a reminder of how important it is for us
to think about the risks and the benefits of the various choices that we
have and how we talk about those risks and benefits with consumers.

(Slide.)

I think one of the things thats really significant about this meeting
is that it signals to me the beginning of a new era. I think that the
CVM approach makes a very important critical first step in recognizing
and creating a context for managing antimicrobial resistance risks, and
this is a in some sense historic occasion in the sense that we are now
really putting together a formal regulation and trying to figure out how
to deal with these in a formal way.

I think that is significant from a risk analysis perspective. Because if
we think about the role of risk analysis in understanding uncertain
risks, I pulled out two quotes. One from a non-risk assessor and one
from risk assessors. The non-risk assessor, Samuel Clemens, who has this
wonderful quote, I was gratified to answer promptly, and I did. I said
I didnt know. Well, thats not helpful. When we need to deal with
managing uncertain risks its not helpful just to say, Oh, I dont
know. Okay? What we need to do is try to put together what we do know
into useful models, try to get together on what the issues are, and try
to make sure that were putting forward our best efforts to create
understanding.

This is kind of captured better in the second quote, which is from two
people who do do risk analysis, where they say that The risk assessor
does not have the option to say, I dont know in response to a
legitimate question. He must state the answer as well as he can, with
all the uncertainties, and state clearly his assumptions. I think that
is the challenge for risk assessors in this debate.

Now, where I think we stand on the current document is I think its a
great first step, but theres still a long way to go, and Im going to
spend the rest of my time talking about that.

(Slide.)

First of all, what were trying to do here is model risk, and we use
models because we cant use actual scientific information to really
understand this perfectly. So one of the quotes thats just a wonderful
one from a statistician from George Box is that All models are wrong,
and some are useful. I think thats an important thing to keep in mind,
and this is a framework which is promoting a very broad, general model.
I think there are going to be some cases where the model is better than
in other cases, and thats something that really needs to be considered.

Now, another adage that people talk about, one that is often attributed
to Einstein, is the notion that a model should be as simple as possible,
but no simpler. The idea with a model is you need to be able to capture
all of the important inputs as they relate to the outputs of interest.
Now, my own take, I was asked for a comment, is that the CVM model is
very simple. I mean, I think the explanation that we had this morning,
in spite of all of the ambiguity in the definition and all of the terms
that are there, it really is a very simple model. There are three inputs
for one output for this resistance threshold, and it basically assumes
that the proportion of the resistant isolates in animals that are
measured, this h, small h, leads to a measurable health impact, meaning
with this large H(x). Okay, and the resistance threshold T(x) can be set
by multiplying the ratio of small h to big H to some unacceptable level.
So I think the critical things here \-- Im going to go back to the
slide that Ms. Bartholomew showed a minute ago.

(Slide.)

Note H in this H(x), large H(x), in this case is measured as the current
estimated prevalence of illness caused by animal derived food, and in
parentheses, annually. This is something that they have noted they are
looking at annually, times the percent of the human cases that are
treated with the antimicrobial drug of concern. Now, the model \-\--
doesnt really say very much about how large H(x) is, quote, measured.
Okay? Or how small h(x) is estimated, and I think those are some of the
big uncertainties that people have about this particular model. But the
model is really very simple. I mean, its basically a line, and so the
whole process is whats the slope of this line, and then whats the T(x)
that were going to use, capital T(x), that were going to put on that
left axis to go across to hit the line to go down to find the small t?
Thats pretty simplistic in my opinion.

(Slide.)

Now, I think that there are a lot of questions that we need to ask about
this model. The first one is, is the relationship really linear? Im not
sure that the talks on Monday would lead us to believe that in all cases
that might be used for the \-- or in all cases where this framework
might be applied we might believe linearity. So I think thats a big
question.

Okay. Second is do we believe the implied causality? Do we believe that
the small h, the amount that we think is in the animal product that is
resistant is actually leading the health output or the health effect
that were measuring for example with NARMS?

Okay. And I think one of the other big questions that would be really
helpful for people to think about in the context of this kind of a
meeting where were in some sense being asked to pick what capital T(x)
might reasonably be is what do we know about the current capital H(x)?
What do we know about the current \-- let me go back.

(Slide.)

The current units on the Y axis. Okay? If you look at figure four in the
document, there are no units there. Are we in the percentile range
between zero and 100? Are we in the range of zero to 1(10^-8^)? Where
are we there? Because that will determine what reasonable numbers we can
pick for capital T(x). Okay? So I think one of the things thats really
important for the Agency to do is to take a step back. Take the NARMS
data. Take some other information that they have about H(x) and figure
out, well, what are we looking at, both with respect to the xs, and I
think those are going to be very different.

I have to say I think enteric disease and systemic disease and some of
the other outcomes that might be possible which might include death are
very different to people, and the risks are going to be different.
Right? We know that some people \-- we know that there are a lot of
people who get diarrhea who never go get treatment because its not
severe enough. There are a few people of the subset who get sick who
actually go to get treatment. There are few still who get very seriously
ill and get hospitalized, and fewer still, maybe none at this point, who
die. Right? So we know theres this whole spectrum of outcomes that
occur associated with microbial or bacteriological pathogens.

So how is it that we capture that spectrum, and what are the outcomes
that are of interest? So I think its hard for people like me to come in
and say, Well, whats a reasonable T(x)? Whats a reasonable threshold
for us to be coming in on on that X axis? I mean on the Y axis, Im
sorry, when in fact its hard for me to know what X is and its hard for
me to know where we are? I think thats the really important thing to
be thinking about.

(Slide.)

So in addition to that, thats really what this bottom point is,
specifically where are we in the context of the existing risks? But
also, does this risk criteria of one in a million make sense in this
case? I mean, I think thats a discussion that, you know, were starting
to open up here, and I think thats the discussion that started
yesterday and will probably continue today.

But I would also raise this question of, Well, how does this risk
compare to some of the other food risks that people might face? How can
we help people understand how significant these risks are compared to
some of the other risks that they might face and, you know, keep it in
the food domain, keep it in the context of some of the other risks that
they face in this area? But if you are going to ask people to make some
choices about tradeoffs between different types of illnesses, you know,
it might be helpful for them to have a better sense about these things.

Again, I think this was just raised by the last question. This model
does depend on all underlying factors in this system model remaining
stable over the period of interest. I think one thing that would be
interesting is to go back to some of the cases that have been
historically evolving and looking at the question of stability and how
does stability change, and what do you see when stability changes. So
what happened with penicillin? What is happening or happened with
tetracycline? What are some of the things that we are observing about
stability or can observe about stability that might impact how we make
assumptions about the way that this model might or might not remain
stable? And in fact can we distinguish between different situations
where stability is a good assumption or an okay assumption and
situations where we might be much more concerned about that assumption.
Can we think about, okay, there are just some things that we know might
have particular reservoirs that just might take off, and, you know, we
may need to think about that.

Now, I think the other thing is that, and the document does a wonderful
job of conveying the CVMs understanding of the complexity of the
problem, and I think the challenge here is that the model doesnt quite
capture those things. I mean, it seems to me that H(x) should be
dependent on a number of things that are mentioned in the document,
including the pathogenicity and the virulence of the resistant bacteria,
hygienic practices, consumption patterns. This includes the very sticky
issue of foods that are imported. How are we going to manage that issue?
Use of the antimicrobial drug or related drugs for human medicine,
dynamics of people contracted resistant bacteria while traveling outside
the U.S., cross-drug resistance, et cetera, they are all of the issues
that impact H(x). So one question is, How is it that H(x) is getting
determined as a function of these other variables, or where are they
coming into the model?

Now, I also want to go back to this point that the severity of the
illness might also matter a lot in the context of deciding whether or
not to monitor for values exceeding the threshold, whether or not to
take a regulatory action. One of the things about the method that is
commonly used in medical decision making where the process is to try and
go to a metric \-\-- quality of adjusted life years is that the analyst
focus on looking at quality of adjusted life expectancy gains. So how
much life expectancy do you gain, or how much improvement in quality
over a certain period of life do you gain, and one of the things that
they have to do in the context of those analyses is identify the various
health outcomes that people might experience and then ask people what
they think about those health outcomes. So how do they compare them? How
do they think about the difference between two days of diarrheal disease
or six days of diarrheal disease. Okay?

So I think one of the things that might be very helpful in the context
of thinking about the values here is thinking a little bit about how do
we think about peoples preferences for different types of health
outcomes that might be important, and that might be an area of work that
you might want to start now. I mean, its going to take some time to get
there, but I think in the long run it will be important to think about
getting there and starting to design those things.

(Slide.)

Now, I think one of the things that raised yesterday afternoon is that
the model mentions variability and uncertainty, but it doesnt really
address them at this point, or it doesnt say how they will be
considered. So I just want to remind everybody the variability and
uncertainty, they are just a function of the decision context. It
matters. The questions in framing are really significant in any risk
management decision-making process. So whose risks are we looking at,
and the risk of what are critical questions.

I think one of the things that Ill just remind people about is that the
National Research Council, the report from 1994, Science in Judgement
and Risk Assessment, did a really good job of conveying the very
different ramifications of uncertainty and variability in the context of
decision making. It said that Uncertainty forces decision makers to
judge how probable it is that the risks will be overestimated or
underestimated for every member of the exposed population, whereas
variability forces them to cope with the certainty that different
individuals will be subjected to risks both above and below any
reference point one chooses.

Those are very different constructs and concepts, and I think that those
are things that need to be considered. I think if you look at this
notion of, well, we could take the 95th percentile and that would be,
quote, unquote, more conservative than the mean, we could argue about
that from a statistical standpoint. Because it is entirely possible with
random variables for the mean to exceed the 95th percentile. So thats
something that, you know, you need to maybe think about. Thats
particularly true if in fact you have a lot of variability in the
population, or even in the context of you have a lot of people who are
at low risk and you have a very small group who are at very high risk;
and then you average those things, and then you can get the mean above
the 95th percentile.

Okay? But its all because there is a certain select group of people
that youre averaging in with everyone else, and you may, if youre not
thinking about it, in a way that you need to think about it in order to
target risk management strategies appropriately perhaps for that group.
You may miss the opportunity to actually optimally manage risk. So I
think this issue of how do we think about variability in the population
as well as how do we deal with some more of the complexities and
uncertainties are really important ones for this CVM to think about in
its next iteration.

Now, in the context of what we know about risk communication and social
amplification of risk, one of the things to really think a lot about is
can we anticipate identifiable people at higher risk. Because one thing
that we do know if you can identify people who are at higher risk and
those people are able to organize, that can certainly lead to large
ramifications with respect to management. So I think to the extent that
you can identify people its very important to think very specifically
about risk management activities that might influence them and also to
think about how your risk management strategy deals with them.

(Slide.)

So I want to transition a little more to some questions about risk
management, but so I dont bore you, I will just put up a couple more
cartoons. This should say of course, Be a bug, see the world. It says,
Be a virus, see the world, by Gary Larsen. The top part is, Youre
from France? Wow! Say, you have lovely eyes. Then the bugs in the
bottom are saying, Hey, everyone, were going to Paris.

(Laughter.)

And I think, you know, what I really want to raise here is there is this
whole issue of the globalization of risk and how the management issues
that we decide for our own domestic agenda do have impacts on the rest
of the world. You know, I think there are a lot of questions here about
how do you think about the imported foods, how do you deal with people
who are traveling, and how do you deal with some of the risky behaviors
that people pursue?

(Slide.)

Another cartoon. So this is the hamburger saying, Go ahead. Make my
day. You know, how do we think about talking to consumers about some of
the things that they can do more effectively? I mean, I know that thats
not something thats CVMs job, but maybe, you know, in the context of
working with the other agencies, making sure that theyre aware that,
you know, you guys are facing some of these other problems, too.

(Slide.)

Okay. So thinking about risk management. Heres some questions that I
think are ones that I would encourage the CVM to think about in the
context of its next steps. The first one is will this, quote, added
safeguard \-- Im using the language from the document. This approach
is being put forward as an added safeguard, and the question is, Will
it help? Okay. So here are the particular things. Does it help us in
particular types of cases? Will it help us uniformly for all pathogens?
I think if you read the document itself you see that the agency has
identified particular things that its already concerned with.
Campylobacter and salmonella stand out, but there might be other ones
where, you know, this is a particularly helpful framework. I think if
thats true then we need to identify those cases where we think that the
framework is important and helpful, and also those where its probably
not so helpful.

Okay? And make sure that the model allows for the distinction. Make sure
the framework appreciates this distinction. This is all implied right
now, but not very explicit, and I think that some of the comments that
we heard yesterday about, you know, certain types of antimicrobials that
are not used at all in humans, you know, maybe the model should
accommodate those just popping out automatically. Maybe not, but I think
some additional thought is helpful there.

Then I think the other thing is, well, it would be very important for
the Agency to start looking more specifically at the process that
theyre going to use to actually implement it. So lets see a few cases.
Lets see, you know, some investment in the details of actually
implementing the model and some iteration on the process.

I think another question that is a significant one that needs to be
raised is, what are the potential risk-risk-trade-offs or the unintended
consequences that might emerge from this, and how should they be
considered? I mean, I think one of the possibilities is this has an
impact on how people do or do not treat sick animals. That might impact
when they send animals to slaughter, or where they send them for
slaughter, or who eats them. I mean, there might be a lot of things that
are changed by a change in policy, and I think its very significant and
important to think about what are the repercussions of whatever actions
we do take, and to think about them very broadly.

Also I think in that same spirit, what are the incentives that currently
exist for all the stakeholders in the process, and how does this change
them? How does it create incentives, or eliminate incentives? How does
it change the perceived responsibility of various stakeholders in terms
of how risks are managed? How does it create incentives or not create
incentives for them to provide information to you that would be helpful
to you? Or, how does it create or change incentives for new drug
development? One of the issues that you might want to think about here
is does this policy make the regulatory climate one that makes the
creation of new drugs for animals one thats no longer good business,
and does it change the market in a way that it could actually ultimately
be detrimental to animal health, to public health, et cetera? So some
thought about those are important.

Okay. And then one of the last things about the thinking here is, you
know, how do the risk management strategies that we use impact the
system? So what about irradiation? What if we started suddenly
irradiating everything? How would that change all of the risks? How
would that change the need to manage various risks here? And, you know,
how do we think about targeting risk management specifically?

(Slide.)

Now, the last couple of slides I have is Are we done yet? And I think
you have heard from my tone were probably not done yet, but I want to
just provide a couple of slides of criteria. This one is from again the
1994 National Research Council report. The idea here is you keep doing
risk analysis. You keep iterating until \-- or you develop the ability
to conduct iterative risk assessments that would allow improvements to
be made in the estimates until 1) the risk is below the applicable
decision-making level, 2) further improvements in the scientific
knowledge would not significantly change the risk estimate, or 3) the
stakes are not high enough to warrant further analysis. I think
according to those criteria more iteration is probably needed in this
case, and I think, you know, this is a great beginning.

(Slide.)

I have another slide which talks about the importance of uncertainty
analysis. This is from Adam Finkel. The best uncertainty analysis is
one that reveals, according to any criterion the decision maker selects,
which of the available decisions performs best, points to priorities for
obtaining new information if time and resources permit so that the
decision maker can reduce uncertainty and increase the confidence he can
place in his choice, spur on the search for new decisions which may
outperform any of the ones that were originally compared. I think
thats where we are. You know, we need to get some more quantitative
uncertainty analyses going.

(Slide.)

So where I am. I think were at the beginning. I think this is a very
important place to be. I think there will be a lot of need for education
and risk communication in this area so that the stakeholders can engage
productively. But I will just close with the comment from Winston
Churchill, Now is not the end. It is not even the beginning of the end.
But it is, perhaps, the end of the beginning. And I would congratulate
the CVM for a terrific beginning. Thank you.

(Applause.)

MODERATOR LATHERS: Thank you, Kim. I think youve raised a lot of
interesting questions, and we will be discussing the answers to some of
these, or let us say proposed answers to some of your questions, during
the panel discussion.

***[Comments on CVM Approach: Microbiologist Perspective]{.underline}***

***By Dr. Daniel Sahm***

MODERATOR LATHERS: Our next speaker is Dr. Daniel Sahm. Dr. Sahm earned
his Ph.D. in microbiology and immunology from Oklahoma University. He
did post-doctoral training at CDC. Dan is currently Chief Scientific
Officer, information services at MRL. He has done a lot of research
focused on antimicrobial resistance and its detection in clinical
laboratories. He has worked in surveillance and, of course, in the field
of resistance mechanisms. Dan discovered the first strain of
vancomycin-resistant enterococci. He will now discuss a microbiologists
perspective on the CVM approach. Dr. Sahm.

DR. SAHM: Thank you.

(Slide.)

Just to give some perspective on the focus of the presentation, I want
to make it clear my background is in clinical microbiology as it deals
with human medicine and antimicrobial resistance among human pathogens,
but Ive been asked to comment on them regarding the microbiologists
perspective on this modeling.

I think its an oversimplification, but something that everybody
realizes, that resistance is a complex microbiological and ecological
issue. Therefore the presentation that Ive put together really focuses
on the concerns regarding the strategic decision to use risk assessment
modeling to address the issue; and I think from a microbiology
perspective what Id like to do is really support what Kim just said,
and that is currently the model, if the model is the way to go, is
really quite too simple.

(Slide.)

Let me first begin by taking a look at what we know or dont know about
resistance amongst human pathogens. With all the focus and attention in
this area over the past few years, there are many \-- there are very
few, if any, definitive answers with regard to how truly prevalent
resistance is, how much it really differs by different geographic
regions, patient types, or what have you. Although some surveillance is
starting to shed light on this. What are the real causes of resistance?
Is resistance changing, and at what pace? To what extent is different
resistance patterns even clinically relevant? Can they be prevented or
controlled? Should they be prevented or controlled depending on the
clinical relevancy? Some of the proposed interventions that have come
out we dont even know if theyre sound ones because they have not been
tested in terms of outcome and results of these interventions.

(Slide.)

In terms of the complexity of resistance among human pathogens, if we
could just focus there for a few slides, and its ultimate impact on
human health, this whole situation results from several complex
interactions of human physiology, medical practices and advances, health
care finances and policies, social and economic variables, and then on
the other side of the coin you have microbial genetics, physiology, and
ecology. A lot of different factors that are very hard to perhaps even
capture in some of the models.

Now, there are others I know. Dr. Lipsitch is going to be discussing
modeling to some degree here, but there are others attempting to model
this resistance in the human population. One thing that is becoming
clear early on in some of the modeling attempts is that a model may even
have to be drug organism-specific, let alone a model that could be
applied broadly to the microbiological ecology in general.

(Slide.)

Theres a couple of \-- Im a little bit afraid that Im going to be
accused of going against motherhood and apple pie here, but I would like
to take a couple of slides to defend the fact that antibiotics use alone
does not necessarily lead to resistance. This speaks to the fact that
theres a lot of complex interactive factors that come into

\-- must come into play for the emergence of resistance, many of which
we still do not understand. I would even challenge that if we had
practiced appropriate use, whatever that really is, from the inception
of the antimicrobial era, how do we know we wouldnt still be in the
same situation we are today?

The other question is perhaps depending on the microbial population that
were studying and the drug thats being looked at, every antibiotic
thats developed might have an extended life span or life expectancy
that can be perhaps not thwarted, depending on how the bug is used or
the drug is used in the population. So we really dont even know what
kind of intervention could successfully prolong the use of our
antibiotics. Just some provocative points.

(Slide.)

But in terms of antimicrobial use alone not equaling resistance, what
Ive chosen here is penicillin use in the human population thats now
celebrating nearly a 60-year anniversary. Ive taken a look through our
surveillance network at four organisms that commonly inhabit mucosal
surfaces in the human population.

Down this branch here staph aureus has achieved about 90 percent
resistance of this drug, and the respiratory pathogen, strep pneumoniae
thats gotten a lot of attention in recent years, is approaching around
25 percent resistance.

If we go down the other arm here, strep pyogenes and enterococcus
faecalis, two other organisms that probably have been exposed to just as
much penicillin as these two given that these organisms both colonize
humans, pyogenes still has not emerged as resistant to penicillin at
all, not in the past and not currently, and enterococcus faecalis
resistance in this organism is still exceedingly rare. Therefore, the
point can be made that just use alone does not necessarily lead to
resistance. Theres a lot of microbial factors obvious that come into
place.

(Slide.)

Given the fact that there are many complexities that we dont understand
in terms of the interplay that leads to the emergence and dissemination
of resistance in the human population, although I am not a veterinary
microbiologist, I would suggest that probably these complexities are no
better understood in this world. Its hard for me as a microbiologist to
quite understand how drugs that enter here and are used here lead to
risk in the human health issues in the human population. So the real
issue here that I have is \-- and Im not a modeler. I admit that. But
the question that I really want to address is how close can a model come
to helping us understand these risks versus other measures of \-- other
methods of measuring the risk?

(Slide.)

One of the things in the document is, and I will address foodborne
pathogens in a minute, but one of the concerns in the document or one of
the issues in the document is that in fact use of drugs in the animal
population can lead to the emergence and dissemination of resistance
factors in what are concerned commensal organisms. Then they can serve
as a vector or a reservoir, if you will, for passing those resistant
organisms or resistance factors on to human pathogens.

What weve done here is queried our surveillance database looking at
some key human pathogens and see what as of the year 2000 what the
resistance rates are these, and then just examples on the right-hand
side are some of the antimicrobials that are used in the animal world,
if you will. Basically the point I want to make here is that probably
due to a lot of use or medical practice, if you will, in the human
population we have a substantial resistance population to many of these
classes of these drugs already amongst our human pathogens, or human
commensals, depending on the status of the patient.

So the question is how do we measure the impact give this, that is
probably much due to the use in clinical medicine of antibiotics, how do
we measure the impact or will we be able to measure the impact that any
kind of drug of the same class thats used in the veterinary world would
have on this population?

Then theres also some anomalies here. For instance, in the United
States among enterococcus faecium in our hospitals, 73 percent are
resistant to vancomycin. As you are well aware avoparcin, thats been
impugned if you will as one of the causes of the emergence of vancomycin
resistance in enterococci, has not been used in the United States.

I think another example to ponder, and I dont have answers for these,
but its just food for thought if you will, one of the other things to
ponder is the issue about virginiamycin. Were concerned about
virginiamycin use in animals versus the emergence of resistance strains
to synercid, a newly released anti-gram-positive drug. I think one of
the concerns I have about synercid resistance is the fact that
enterococcus faecium is initially susceptible to that, but a much more
commonly clinical relevant enterococcus species, enterococcus fecalis is
intrinsically resistant to that drug, faecium and fecalis reside in the
same gut, and I think theres probably a much bigger threat of somehow
the fecalis capability finding themselves in the faecium then perhaps
there is from a drug use in the animal population finding a way \-- its
way through the animal population to the human population and then into
enterococci faecium. But it could go either way, I admit, but its just
food for thought.

(Slide.)

With regard to the impact of veterinary antimicrobial use on resistance
in human pathogens of animal origin, I would like to really focus on
salmonella, and perhaps during the discussion period we could talk about
the campylobacter issues. But basically this is what is in our database
that we collect from humanized lists and report in the clinical
laboratories from 1996 to the year 2000. We take a look at ceftriaxone,
third generation cephalosporin resistance, and ciprofloxacin as the
indicator for fluoroquinolone. I just put in parentheses examples of
veterinary drugs of the same class.

You can see that while ampicillin resistance is quite common in
salmonella what that tells me is that over time with beta lactam
exposure to this genus through either veterinary use or what have you,
these organisms have emerged with resistance to the beta lactams.
However, ceftriaxone in our population resistance is very uncommon,
regardless of the body site, and ciprofloxacin resistance overall is 0.1
percent, 0.2 percent in blood, 0.5 percent in other sources. Basically
the point here is that since we monitor these things on a regular basis
we have not encountered a ciprofloxacin-resistant salmonella in our
database since 1998. Now, I know there are other outbreaks that Fred
Angulo and others and working on. So it does occur. Im not trying to
suggest that it doesnt happen, but this is the relative burden of
resistance at least through our medical \-- or surveillance of human
pathogens currently.

(Slide.)

Now, many of you are probably aware of the paper that appeared in
[JAMA]{.underline} where it was established that from 1996 to 1998 13 of
4,000-plus isolates of salmonella were found to be cetriaxone-resistant
and mediated by CMY-2 beta lactamase, and Ill talk a little bit more
about that in a moment. But the risk here is \-- the prevalence here is
about 0.3 percent. So based on the data from the previous slide and that
of the article that appeared in [JAMA]{.underline}, the phenotype
remains very, very rare, and the question that I have is can the risk of
such a rare event to human health be accurately modeled. Just again more
food for thought, and we can discuss that later.

The other question I have is what is the cause and the path of
development of this resistance really known, and if you will allow me to
just go through wonderings of my own here, however inaccurate or unusual
they might be.

(Slide.)

Some characteristics of this mechanism of resistance in salmonella, at
least as it has appeared in the United States, CMY-2 as an AmpC-type
beta lactamase. That is it is a chromosomally-based beta lactamase
intrinsic to citrobacter. That is almost all citrobacter freundii house
this gene in their chromosome. The CMY-2 as an enzyme that has been
located or transferred by some mechanism to a transferable plasmid, and
therefore it is felt that the way that this has found its way from
citrobacter to other organisms is by plasmid transfer from citrobacter
to e. coli and klebsiella.

This has been well documented in the past to happen amongst human
isolates, and now what has apparently occurred is that from citrobacter
it has gone into salmonella in veterinary isolates, an animal source.
The AmpC enzyme is rare among salmonella still. But again the major
reservoir is that it is an intrinsic characteristic of citrobacter
freundii.

(Slide.)

Now, if we take that, if you allow me a couple of potential pathways to
consider here. If you have animals and human transfer of resistance or
risk, one could say that perhaps the pressure of ceftiofur usage really
selected for citrobacter freundii to transfer and salmonella to accept
this AmpC beta lactamase and therefore you have had this pressure for
the kind of resistance to emerge against this drug. The question is
whether or not all the beta lactam usage in the past could have lead to
similar events, and whether or not its possible to incite \-- or to
indite, excuse me, the use of ceftiofur itself.

Its also completely possible since these organisms coexist in the same
environment that AmpC transfer to salmonella is a completely spontaneous
event and that in fact the reason that the AmpC contained in salmonella
found their way and established themselves in the human population is
because of human cephalosporin use, and this is where the selective
pressure may come from.

(Slide.)

A little bit further stretch might be in fact that you accept a
ceftriaxone-susceptible salmonella and that in fact the AmpC that
resides in citrobacter or similar enzymes in enterobacter in the human
gut transfers to salmonella. So the transfer of AmpC to salmonella in
humans may be a sporadic event that may or may not be linked to the use
of beta lactam use in human medicine, or it may or may not be linked to
beta lactam use in veterinary medicine. Its a very difficult task to
exactly track.

(Slide.)

Now the findings by Fey et al would really bring into fact the drugs \--
the bugs all already resistant in the animal population because by using
molecular typing they have documented the apparent transfer of CMY-2
containing salmonella from livestock to a child. However, interestingly
not all the isolates of salmonella that were investigated contain this
plasmid. Therefore, the penetration was not extensive in the salmonella
species, and maybe this does speak to a sporadic transfer from a
citrobacter to the salmonella. Then also in the same study interestingly
they could not establish whether or not cefiofur use, the cephalosporin,
they could not establish its use in the herd.

(Slide.)

So if you will just let me outline then some of the issues, the findings
of Dume and Fey document that in fact ceftriaxone-resistant salmonella
exist and may be, albeit rarely, encountered in humans in the United
States. However, neither report was a cause and effected related to
animal or human use of cephalosporins clearly established.

(Slide.)

Without being able to model the emergence of resistance in either human
or animal bacterial populations, how can we expect to reasonably model
the risk of drug use in the animal population to human health? Again,
questions. With regard to human health, the model approach is further
complicated by the fact that the vast majority of salmonella infections
for the vast majority of antimicrobial therapy is not required.

Also, how does the fact that there are other treatment choices play into
the risk assessment? And one of the other assumptions made in the
document is that these patients would receive \-- that have a resistant
or a susceptible salmonella, they would receive the same treatment, and
I dont think as a clinical microbiologist thats necessarily true.
Because these organisms are tested against a battery of drugs, and there
is a likelihood that another choice would be used to treat serious
infections. Im not trying to minimize the importance of this type of
resistance, but in terms of risk assessment modeling there are \--
ceftriaxone-resistant salmonella is not the end of the road
therapeutically for our choices necessarily. Its something to be
concerned about, but in the risk assessment model how does that play
into it?

(Slide.)

Therefore, with the microbiological, ecological, and genetic phenomena
surrounding the resistance not being well understood for human or
veterinary medicine, alternatives to risk assessment models should be
seriously considered.

(Slide.)

I think that along the lines of suggestions here, what was mentioned
earlier, I think a more extensive and timely surveillance, e.g.
bolstering the NARMS initiative, would provide one such alternative more
or less to get to the reality of where things are rather than trying to
only model. Im not saying this is instead of modeling, but I think that
more time \-- the capabilities that we have for more timely and
extensive surveillance gives us the chance to collect more information.

We can monitor population shifts in MICs over time to detect subtle but
perhaps significant shifts in the level of resistance prior to the
emergence of frank resistance. We can identify new resistance profiles
in the context of the environment, what was the drug use pattern, what
were other factors that may have lead to this phenomena. Then we put
resistance that is found in a clearer context of prevalence. Geographic
distributions and what have you that help us avoid unnecessarily
sensationalizing any rare events or findings.

(Slide.)

So I think that basically what were talking about here is that we
really bolster surveillance initiatives on the part of NARMS and perhaps
others this can be a key step in the process of gaining the knowledge
necessary for making informed decisions. You get the data collection,
management organization, and that information is then used in the
analysis and even can be fed into the modeling approaches that are
developed. But basically this flow of more real information for the
development of knowledge is probably a way that I would lean in addition
to, if not in place of, strict modeling. Thank you.

(Applause.)

MODERATOR LATHERS: Thank you very much, Dan. You, too, have raised
additional questions for consideration during our discussion period.

***[Strengths and Limitations of Threshold Approach]{.underline}***

***By Dr. Robert Tauxe***

MODERATOR LATHERS: Our next speaker is Dr. Robert Tauxe. Robert Tauxe
earned an M.D. from Vanderbilt Medical School and holds an M.S. in
public health from Yale University. He completed an internal medicine
residency at the University of Washington and spent two additional years
of training at CDC. He is currently Chief of the Foodborne and Diarrheal
Diseases Branch in the Division of Bacterial and Mycotic Diseases at the
National Center for Infectious Diseases at CDC. He will discuss with us
the Strengths and Limitations of the Threshold Approach. Rob.

DR. TAUXE: Thank you very much, and its a pleasure to be here this
morning. Im delighted to be here and to see and to take part in this
important and welcome discussion. Lets see. Im not sure the projector
is actually turned on. Is it? Thank you very much. Its also not quite
pointed at \-- well, I hope you can hear me all right in the back. Yes?
Good.

(Slide.)

I have some comments on the issues of the thresholds that were looking
at, thresholds for resistance, thresholds for human harm, both.

(Slide.)

I think I want to start by congratulating the Center for Veterinary
Medicine on holding this meeting and launching this public dialogue on
an issue that I think confronts us in many different parts of medicine,
both veterinary and human, and in this particular focus on the
intersection of the two.

Antimicrobial agents of course are critical to both human and veterinary
medicine, have a particular property that makes them different from
toxic substances. This has been treated extensively yesterday of course.
Using them tends to select full resistance to them that was not present
as a problem at the point when they were first begun. So theres a cycle
of enthusiastic testing, excellent results, and ultimately often a loss
of efficacy at some point down the line.

Our challenge is that we anticipate that there may be harm in the form
of that resistance which emerges which is not demonstrable when the
agent is first approved, and therefore we need the strategy to recognize
and mitigate the harm when it is \-- that we anticipate. Define
thresholds to control the anticipated harm are the solution that we are
very interested in exploring and seeing.

(Slide.)

Now, the threshold approach in its essence would be to monitor for the
appearance of resistance that will affect the public health in the
bacteria in food animals, and bacteria in the food itself and the
bacteria thats isolated from humans. The bacteria being of course one
subject of discussion, those of primary interest to us I think, are the
foodborne pathogens. Certainly campylobacter and salmonella, with
interest in other bacteria as well.

Resistance is also to a group of agents of concern, and I think it is
going to be very important to have the input of the human infectious
disease specialty community assisting in the definition of which those
agents of concern are and then set points that identify harm to the
public health linked to mitigation or control actions that are triggered
at those set points.

(Slide.)

Now, as has been summarized here already, I think the great strength of
having thresholds agreed upon is that there then are straightforward set
points for control efforts for mitigation or for withdrawal that use
publicly available surveillance data. They are visible targets available
to all, and has been discussed rather extensively, and I dont plan to
dwell on this, there are two sources of surveillance data now for human
health. The NARMS program being one, and FoodNet providing additional
information about case treatment and case management being a second
source. Of course there is also the source of monitoring data,
potentially surveillance data for isolates from meat clinical animal
isolates in the current NARMS function and the expansions that have been
discussed.

(Slide.)

Now, I think the NARMS, the human portion of NARMS, is a national sample
of clinical human strains of salmonella, campylobacter, e. coli, ON5787,
shigella, and enterococcus weve expanded to, and then there is also the
collection of animal isolates of salmonella from the slaughter isolates
obtained from SSIS and the various clinical and research isolates that
are also submitted and are typed and the antibiotic resistance is
determined. I think there is an important extension adding the
collection of samples from retail foods and from an expanded array of
feed stuffs and foods that are not currently covered here that is
anticipated and I think is important.

(Slide.)

Now, the data from FoodNet come from our nine-site active surveillance
system for diagnosed cases of specific foodborne infections, including
salmonella and campylobacter as well as others. This is a collaboration
between CDI, state health departments, USDA, FDA as part of that active
surveillance that is of identifying cases of these infections that are
diagnosed in the clinical laboratories in the sites where surveillance
is going on. We have also conducted special studies. Case studies, for
instance, that allow us to measure the frequency of treatment with
specific agents, the frequency of hospitalizations or complications in a
series of cases of a given infection, and also conducted special
studies, case control studies of sporadic illness to determine risk
factors for infection and in some cases for resistant infection,
including the measurement of illness promotion by the treatment of other
infections, a subject I want to return to.

(Slide.)

Now, some of the concerns voiced about the threshold approach are first
of all setting a threshold and saying resistance will rise or impact
will rise to a certain point and then definitive action would be taken
presumes in fact that resistance is reversible, and thats a very \--
thats a presumption that we probably need to think about. Bacterial
antimicrobial resistance may not, as has been suggested already,
disappear nearly as rapidly as it emerges, and this argues that
thresholds would need to be relatively conservative, relatively low, if
the harm once it appears is going to not rapidly reverse. Of course,
there is the major challenge in setting what acceptable levels of harm
are.

I think extensive discussion yesterday was very helpful in that regard.
Again, it argues for basing thresholds on resistance levels rather than
simply on the numbers of persons who are harmed or seriously harmed, and
that using indicators of harm that are based on food or isolates from
foods or food animals provides a more early warning than waiting for
isolates from people. In the comments of the previous speaker this is
the essence of trying to predict harm in the future when we do not have
data, and I think that if we wait until we have large numbers of
seriously ill people or even failed cases on which to construct a model,
the more of those there are the more accurate and simple and direct and
obvious the model will be and the more we will have failed to protect
public health. So our challenge here is that we are trying to predict
future harm, and if we wait until we have lots and lots of harm with
which to construct accurate models, we will have waited much too long.

There is of course another concern, is that there is a lack of some
systematic data on pathogen resistance in the concentration levels in
food or in animals from a range of foods, and that there has to date
been relatively little comparison of those isolates with isolates from
people in a detailed way.

(Slide.)

I want to talk a little bit about types of harms, because I think this
gets at exactly what we need to be looking at. There are two types of
harm I want to talk about. One is the one that has been the subject of
virtually all the conversation, and that is the direct human clinical
harm. That is infection with a foodborne pathogen which is resistant to
a primary treatment for that infection. The treatment of that infection,
the consequences of that infection, the infection becomes difficult. The
treatment is more expensive and less successful because of resistance to
the agents of choice for that pathogen, and that I think has been the
primary focus and concern.

I want to also mention that there is an indirect clinical harm due to
the effect of disease promotion when you have illness with a resistant
foodborne infection that suddenly complicates the treatment of another
infection in that person, analogous in some ways to
antibiotic-associated colitis caused by clostridium difficile. The
result being that there actually are more sporadic cases of, say,
salmonella and larger outbreaks than there would be if the strains were
not resistant.

(Slide.)

To explore direct clinical harm a little further, and I apologize, this
is probably totally illegible from the back of the room. Lets imagine a
78-year-old woman who is hospitalized with a high fever and a diarrheal
illness. Her clinicians begin empiric therapy with a fluoroquinolone.
One does not wait until the antibiotic resistance results are in. One
begins empiric therapy in serious illness. After 24 hours a little
impact, falling blood pressure. She is started on ceftriaxone and
gentamicin and slowly improves, but has a complicated two-month
hospitalization. This is not an atypical concern here.

The admission stool culture yields salmonella that is shown to be
fluoroquinolone resistant 48 hours after admission. That would be an
example of direct clinical harm. The mechanism of the harm being that
antimicrobial treatment of invasive salmonellosis or severe
salmonellosis or campylobacteriosis can be lifesaving. The initial
choice

of agents is critical, treatment of choice being fluoroquinolones or the
new cephalosporins. If the strain is already resistant to those agents
treatment is less likely to be effective and the medical community, the
clinicians, will need to change to other agents that may be less
effective or more expensive, leading to a longer illness, more
complications, and ultimately higher mortality. The agents of concern in
this entire model then are those that are the treatments of choice for
those infections.

(Slide.)

Indexes for monitoring such harms are available. The persons treated for
the infection with an appropriate agent to which the infecting organism
is resistant is the item we are trying to estimate here. That could be
constructed as a human health index based on the frequency of human
treatment with the agents of choice, the frequency of resistance to
those agents of choice in foodborne pathogens, either isolated from
people or to those same agents isolated from food or food animals. Then
likelihood expressed as the likely number of such maltreatments or
mistreatments among all cases of salmonellosis. High risk populations of
humans, the risk of severe illness suggest the need to build in a safety
factor here. This harm in human medicine would need to be quite rare.

(Slide.)

The arenas where this sort or direct clinical harm could occur, one
obvious one would be the campylobacter resistant to fluoroquinolones
where information from the 1999 NARMS data as to fluoroquinolone
resistance in campylobacter jejuni as measured in NARMS is at 17.6
percent prevalence. In a FoodNet case study approximately 53 percent of
diagnosed campylobacter cases were treated with fluoroquinolone. So its
a typical treatment and a common resistance, and in our special case
control study conducted through FoodNet those resistant infections were
associated with two specific risk factors. One was eating chicken or
turkey and the other one was \-- independent, was foreign travel.

A second arena is salmonella that are resistant to fluoroquinolones or
to ceftriaxone, and this is I think a more recently emerging arena. But
again in our 1999 NARMS data we had a single isolate in the NARMS system
that showed resistance to ciprofloxine, and five isolates then with high
level ceftriaxone resistance. So those are areas where one begins to
have the arena where medical clinical concern will be focused on.

(Slide.)

On indirect clinical harm through disease promotion is a somewhat
scenario. Let me describe a child who begins treatment for a confirmed
strep throat with amoxicillin, and 24 hours later the childs throat is
in better shape. But the child has developed a higher fever, severe
diarrhea and abdominal cramps, and the stool culture at that point grows
multiple-resistant salmonella. The mechanism here is that low-dose
exposure to salmonella often results in a silent infection as the normal
gut flora restrain the impact of the salmonella. But during that time of
two or three weeks of carriage in a parent infection the treatment for
another infection can interfere with the normal flora, and if the
salmonella strain is resistant it has a selective advantage in this
event and can increase and cause illness. Its sort of the reverse of
competitive exclusion, if you will. Here the agents of concern for this
effect are those that are in common human medical use that interfere
with anaerobic gut flora and to which the salmonella may be resistant.

(Slide.)

Here is a graph that is an attempt to sort of visualize this event a
little bit more. If we have time running along the X axis here and the
severity of the illness along the Y axis and a little dotted line, that
means the illness becomes \-- or the infection, the intensity of the
infection, is enough that it produces visible illness or apparent
illness. Then a very low-dose exposure to salmonella may lead to a
transient several-week colonization in some people. Which, during that
time, if there is an exposure to an antimicrobial to which the organism
is resistant it can be converted to an overt apparent clinical
infection.

I think the same happens in veterinary medicine frequently. I know horse
\-- equine practitioners and equine hospitals find this as a recurrent
and constant problem. Its occasionally lead to closure of equine
hospitals, similar problems. Its not unheard of. Its not even rare.

(Slide.)

We can monitor this sort of indirect clinical harm by identifying
persons who become ill with resistant salmonella infections and in
association with treatment of other infections and could construct an
index based on the frequency of human treatment with a specific list of
agents of concern, the frequency of resistance in salmonella to those
agents that are isolated from the humans or isolated from foods or food
animals. For this sort of harm, since we all occasionally now and then
will take an antibiotic, the entire population is potentially at risk,
and the likelihood of that could be expressed as the likelihood of such
an illness occurring among the entire population.

(Slide.)

Arenas where this indirect clinical harm through disease promotion could
occur are basically with the organism salmonella. Where we identify it
and where we have observed it is, for example, salmonella typhimurium,
DT104, which have the resistance pattern, the penta-resistance pattern,
of ampicillin, chloramphenicol, strep, sulfa and tet. At this point,
that represents something like seven percent of all salmonellosis in the
United States and about in our case, a control study, about 45 percent
of these cases appear as complications of treatment of other infections.
Compared to the controls, who are much less likely to have just begun
treatment for another infection.

So this DT104 problem in humans is being potentiated because it is
resistant to some antibiotics where are \-- people are treating with for
other reasons. Were concerned that a similar pattern and a similar
effect may also be emerging with salmonella typhimurium with the AKSSuT
pattern, which represents nearly three percent of all salmonellosis in
the United States, and that same pattern has now jumped and appears in
salmonella newport, which represents about one percent of all
salmonellosis in the United States.

All three of these strains have been described with known bovine
reservoirs as well. The links to specific foods for salmonella
typhimurium have been through outbreak investigations linking it either
to foods of bovine origin or to direct animal contact.

(Slide.)

DT104 and other multiple-resistant infections raise the issue of
multiple-linked resistances. This is a particular challenge Im sure.
The penta-resistant salmonella typhimurium, DT104, and actually can
acquire resistance than that. The indirect clinical higher model if we
were to try to model that would mean using a threshold for those agents
commonly used in medicine to which DT104 is resistant and the use of any
one of several different agents in animals that could provide the
selective advantage for DT104, and mitigation would need to focus on the
agents commonly used that might be contributing to the selection of
DT104. Which is obviously a somewhat longer list than just the
fluoroquinolones and the late generation cephalosporins.

(Slide.)

There are other harms which are less easy to quantify that have been
mentioned already. Because resistant strains will spread more easily in
animals taking antimicrobials than in animals \-- than strains which
were not resistant because of basically the reversal of competitive
exclusion and the lowering of the infectious dose thats been well
documented in mouse models since the 50s. Contamination of food with
salmonella in general may increase, and the result would be a general
increase in the number of foodborne infections.

Another harm potential that is less easy to quantify has been discussed.
The selection of a resistant gene in a commensal food animal with
subsequent transfer to a pathogen in the human host resulting in a
newly-resistant pathogen in the human, and there are other harms that
may not relate to resistance at all, such as the mobilization of
shigatoxin in phage by the exposure of shigatoxigenic e. coli to
low-dose antimicrobials that has been described by David Atchinsons
group at TOPS. The result being a new potential pathogen, an e. coli
that acquires shigatoxin.

(Slide.)

There is the challenge of attributing risk to different foods and to
food animal species, and there are several epidemiological tools
available to do that. That is to allocate, say, a specific sort of
salmonella, how can we allocate the risk of that salmonella to different
foods and food animal species. One is the distribution of sources for
outbreaks. A second is the case control studies of sporadic cases to
determine food vehicles. The third being a comparison of subtypes of
strains from humans and various animal reservoirs.

Denmark has successfully used the last strategy of subtyping to
attribute the risk of various sorts of salmonella across food animal
species, and this is a process that we think is very useful, very worth
exploring. A critical need to do that is going to be systematically
collected strains from foods and comparing those with those found in
humans.

(Slide.)

To strengthen existing surveillance in the FoodNet site is going to mean
collecting routine data on the treatment of salmonella and campylobacter
infections and on the exposure of antimicrobial agents in those people
before the infection was apparent. Its going to mean the molecular
comparison of similar strains from human and animal sources and
increased coverage of NARMS for both human infections and contamination
of retail foods. Beginning its going to importantly include collecting
data on the amounts of antimicrobials, specific antimicrobials used in
animals.

(Slide.)

Let me conclude by saying that thresholds are a real concrete advance to
the challenge of managing an anticipated harm associated with the use of
antibiotics in agriculture, that there are several types of harm that
can be distinguished that may require different thresholds and different
denominators for each threshold. Direct clinical harm would have the
most conservative one. The indices based on isolates from foods or
animals are important to predict harm before it is documented in humans,
and existing surveillance systems can be used to monitor the potential
for harm with some strengthening. Thank you very much.

(Applause.)

MODERATOR LATHERS: Thank you, Rob.

***[Effect in Resistance Following Changes in Antimicrobial
Use]{.underline}***

***By Dr. Marc Lipsitch***

MODERATOR LATHERS: Our final speaker before the break is Dr. Marc
Lipsitch. We are going to learn from him the effect of resistance
following changes in antimicrobial use. Marc earned his Ph.D. in zoology
from the University of Oxford. He completed a post-doc at Emory
University and another one at CDC. He is now an Assistant Professor of
Epidemiology at Harvard School of Public Health. He is doing research
which focuses on the transmission dynamics of infectious diseases in
humans with a focus on antibiotic resistance and on population biology
of streptococcus pneumoniae.

Marcs disclosure includes the fact that he has served as a recent
consultant to Bayer. Marc will now share with us his thoughts on the
Effect of Resistance Following Changes in Antimicrobial Use. Marc.

DR. LIPSITCH: Okay. Thank you very much, and thanks for the opportunity
to come here. As was stated, my particular interests have been in human
\-- in antimicrobial resistance in human pathogens and the rates at
which that changes following changes in antimicrobial use. This, like
many of the subjects that are relevant to the discussion today, is an
area where theres lots of ignorance, a few principles, some data, and
we have to make decisions and policies ultimately based on uncertain
data. So I want to talk a little bit about what is known, at least in
the human arena, and a little bit in the veterinary arena about how
changes in antimicrobial use result in changes in resistance and what
implications that may hold for the threshold issue. Which is something
that Dr. Tauxe just mentioned also a few minutes ago.

(Slide.)

So Im just going to give three very brief examples of the time scales
on which antimicrobial resistance can change following a change in
antimicrobial use in different settings. This is data from a study from
the early 80s by Dunkle and colleagues in a hospital where there was an
outbreak of a gentamicin-resistant, methicillin-resistant,
staphylococcus auereus. That outbreak was recognized in the summer of
1979, and a number of measures were taken to control it, including
isolation of patients who were resistant, other measures to \-- who had
the resistant bug, others measures to reduce transmission and control of
any microbial use.

In that hospital setting what you see is following these changes there
was a rapid decline in resistance. So that from August when the measures
were taken when the prevalence was about 30 percent to December the
outbreak was eliminated and the organism remained absent for some period
after that.

So in that particular hospital outbreak and in a number of other
hospital outbreaks its been possible probably through interventions,
although theres always uncertainty because you dont do a controlled
experiment, but probably as a result of interventions to reduce the
prevalence of resistance in a very rapid fashion. And I might point out
a possible explanation for that before going on, which is based on some
modeling work that weve done.

One special feature of a hospital or an intensive care unit is that
theres a \-- its an open system. Theres a constant influx of people
carrying their own organisms, and because the hospital is a focus of
resistant organisms and the people coming in from the community are less
likely to carry those resistant organisms, theres a sort of dilution
effect as more and more bacteria come in carried by their patients. In
the absence of strong selection for resistance by any microbial use
theres a very easy mechanism by which imported bacteria can sort of
wash out the resistant ones in the hospital. So that may be a mechanism
by which this kind of very positive outcome is possible when you
intervene in a hospital.

(Slide.)

I want to contrast that with what has happened in community-acquired
infections, and the data Im showing here are from a [New England
Journal]{.underline} article a few years ago which was really trumpeted
as one of the best examples in which reduction in community
antimicrobial use can lead to a reduction in resistance. This is
resistance in group A streptococcus in Finland, and the orange line
there shows consumption of erythromycin, which between 1988 and 95 was
reduced by about seven \-- about six fold or so.

In that study they gathered data starting in 1990 on the prevalence of
resistance in group A strep to macrolides, erythromycin and related
drugs, and the article was in the [New England Journal]{.underline}
because it was thought to be an example of a tremendously successful
intervention in which from 1990 to 1996 the prevalence of resistance
went from about 14 percent to about seven percent. So that was
interpreted by the authors as a rapid and good response to changes in
antimicrobial use.

I think from looking at the data you see that there are other
interpretations possible of that. First of all, following most of the
reduction, at the time most of the reduction had been completed, say by
1993, resistance was on the upswing. It wasnt until after that when the
level of use was already quite low that resistance started to come down.
Also, we dont know what the pattern was beforehand. So attributing
cause and effect is difficult.

Then finally, even if you take the trend from 1990 to 1996 as a \-- as
sort of representative of what was really going on, you can see that it
takes six years following this reduction to in use, or eight years if
you start in 1988, to see the reduction of about two fold in resistance.
So its not in the community. Its not the case that you can simply turn
off antimicrobial use and tomorrow see a reduction in resistance back to
baseline levels, and Dr. Tauxe made that point briefly in his talk and I
want to reemphasize it.

So we have two examples. We have hospitals, and we have
community-acquire infections; and in the community theres not this kind
of constant entry of new people carrying their bacteria which are more
likely to be sensitive than those in the community as a whole, but there
is that in the hospital. I think farms may fall into either category,
depending very much on the details of the management practices and the
source of animals on those farms, and they may fall somewhere in
between. But I think that in the interest of conservatism its important
to realize that we cant expect immediate responses to changes in
antimicrobial use.

(Slide.)

Id like my transparency now, if I could. All right. On the transparency
Im going to show some data that are actually from animal use; and these
are from the Danmap study from Denmark, and I want to direct your
attention. Is there any way to \-- well, I guess it cant get much
bigger. So Im going to have to sort of walk you through it. If you look
at the top left there, which is labeled figure 10, that is use of
avoparcin, the glycopeptide relative of vancomycin, and resistance to
vancomycin or avoparcin in enterococcus faecium from broilers and pigs
in Denmark.

The Xs which go down and hit zero are use of avoparcin. Is there a
pointer here? Oh, yeah, here. I got it. So we have avoparcin use going
down to zero here in 1996, and the open circles are the pig isolates and
the closed circles are the broiler isolates. What we see is that
following the reduction in use of avoparcin there was a decline in
resistance to avoparcin or vancomycin, but it didnt go down to zero,
even though the use went down to zero. It stayed at a relatively flat
level for several years.

They worked out what was happening there, and it turns out that what was
happening is that there was a clone of vancomycin-resistant enterococcus
in Denmark in the farm context, and that clone also happened to be
resistant to macrolides. During that period of flatness there was
continuing extensive use of tylosin, a macrolide used in veterinary
medicine, which was maintaining selection for that clone. Although the
selection for the particular resistance had disappeared, that clone that
was multiply resistant was being maintained presumably by selection by
another drug to which it was resistant. It was only after declines in
use of tylosin in 1988 that reductions in resistance began to \-- or
continued down to a lower level.

So that provides one example from the veterinary context which was not
an immediate decline. Its perhaps not as slow as the group A strep
example I showed, and its a case where it required a second
intervention unanticipated by anyone involved I think before you could
actually get the outcome they wanted, which was to get rid of this
vancomycin-resistant clone. Can we turn off the overhead?

(Slide.)

So as a general matter, there are several factors that will influence
the rates of change in resistance after a change in antimicrobial use.
The ones Ive marked in green here are things \-- are factors which can
hasten the decline. For example, if resistance genes or mutations
severely handicap the ability of bacteria to transmit or to persist in
their hosts then as you relax selection there will be a natural
selection for \-- as you relax use of antimicrobials there will be
natural selection, which increases the frequency of sensitive organisms
and causes a decline in resistance.

As I mentioned in the hospital context, if there is an influx of
susceptible bacteria into the system that can have a similar effect. On
the other hand, there are factors which can decline, can slow the
decline of resistance, such as continuing selection by other
antimicrobial agents, which I mentioned a minute ago, or persistent
reservoir of resistant strains. In a hospital that might be one patient
or an environmental contaminated area thats there for a long period and
maintains itself as a source of resistance strains. On a farm I can
imagine, though I dont understand much about how farms work, I can
imagine that broiler houses for example probably arent autoclaved very
often. So there certainly are many places that they can \-- that could
maintain reservoirs of resistant strains.

(Slide.)

So what are the implications of these considerations for setting of
thresholds? Well, the point is the resistance may, but we cant be
certain, may change slowly following any withdrawal or reduction in the
use of an antimicrobial agent. As a result, its important to set a
threshold that would be well below the level that would be a serious
health threat. Or another way to think about it is at a level that can
be maintained for some years without anyone considering that to be a
serious harm. Because we cant be certain that we can get back much
below that level.

Finally, a final implication is that other measures besides withdrawal
of the offending agent may be necessary to make resistance decline. For
example, removal of reservoirs; it may be that cleaning \-- that
disinfection and cleaning out of particular parts of a farm or of a
feedlot that have large concentrations of bacteria may be necessary to
get rid of the reservoir of resistant organisms and possibly changes in
other antimicrobial practices, as in the Danish example.

(Slide.)

I have a few more slides that I want to show, but Ill save those for
the panel time.

(Applause.)

MODERATOR LATHERS: Thank you very much, Marc. We will now take a
10-minute break. I am asking that you be back here 10:45, please, so we
can begin promptly.

(Whereupon, brief break was taken at 10:35 a.m.)

***[Panel Discussion on Resistance Thresholds]{.underline}***

MODERATOR LATHERS: We are continuing with

Session 4 and the panel discussion, Use of Risk Model to Establish
Resistance Thresholds. We have a number of questions which will be
discussed first in a five-minute comment period by each member of our
panel. Once they have finished their discussion of these questions, we
will then open the floor to questions.

Our first question is shown on the screen, and it is entitled What are
the strengths and limitations of the approach to establishing thresholds
outlined in the CVM Discussion Document?

Question two, According to the CVM approach, outlined in the CVM
Discussion Document, resistance thresholds are derived using an
epidemiology-based model. The NARMS program would be integral to
monitoring resistance changes relative to the resistance thresholds.
Based on the model described, it is conceivable that derived resistance
thresholds could be so low as to be below the limits of detection of the
current NARMS program. How to address this problem?

Question three, The CVM approach to the establishment of resistance
thresholds has multiple complexities. CVM recognizes there may be
risk-based methods to establish thresholds other than the approach
outlined in the discussion document. What alternatives exist?

Question four, When an antimicrobial drug will be approved for use in
multiple food animal species, it may be possible to divide the
resistance threshold among the food animal commodity groups according to
the individual commodity groups contribution to all enteric disease.
Would this be a viable approach?

Question five, How should resistance thresholds be updated after
approval to take account of new information? What criteria should
trigger a reassessment of a current resistance threshold or lack of
resistance threshold? And then of course we have the Other Comments.

At this point I would like to introduce a new member of the panel. Dr.
Lester Crawford has just joined us.

Dr. Crawford earned his DVM from Auburn University and his Ph.D. from
the University of Georgia. He has spent 10 years working at the FDA and
at the USDA, and this includes serving as Director of the Center for
Veterinary Medicine. He now serves on the Expert Advisory Panel in Food
Safety at WHO, and he is a Research Professor and Director at Georgetown
University Center for Food and Nutrition Policy.

Dr. Crawford, I would like to begin with your comments. I remind all
panel members to please limit your comments on any of the questions or
any other comments that you would like to make to about five minutes.
Dr. Crawford.

DR. CRAWFORD: Id just like to make a few general comments about the
meeting and the purpose, and what Ive heard this morning and what has
been reported to me about the previous two days. I think there is good
news and bad news about this initiative. One is that CVM is certainly
addressing in a comprehensive way what can only be described as a
serious national and international problem. To say that to argue or to
tilt windmills about whether or not antibiotic resistance is an adverse
reaction or a toxicological effect or however you want to put it is to
me not useful.

What needs to happen is we need to go ahead and put in place something
like a threshold document that will enable us to do it in the kind of
way that we have not done in the past, and I will mention just a couple
of things about that. The bad news is that its virtually impossible to
do, but having said that, here we are addressing the art of the
impossible.

I think a lot of this goes back to some earlier FDA initiatives, and to
me the great impetus for doing this is the kind of ambiguity of those
initiatives and also what can only be called charitably as their
inability to bring the subject to completion.

The first really big initiative was a national news headline thing of
about three years starting in 1979 with what was called the Mercer
Committee. That committee met seriously. It consisted of internal FDA
people and also personnel from the then FDA Advisory Committee, which
was the only one. An interesting thing about it is their principle
conclusion, and that is that useful therapeutic new human drugs should
not be added to animal feed. It was the only real recommendation they
were able to agree upon, and that was split I believe 13 to 12 in the
committee. Thats kind of where we still are today.

The second sort of development came about when following upon that we
entered upon an initiative to restrict the use of penicillin and
tetracycline in animal feed. The same kind of thing happened. What
Congress wanted to know and what the National Academy of Sciences wanted
to know and what the general public wanted to know is How strong is the
link, and, Will there be positive results as a result of taking this
action? Those questions, I would submit, were really never answered to
the satisfaction of the constituencies and the decision makers, and that
may also be kind of where we still are.

At the same time that the penicillin and tetracycline initiative was
going on a much lesser known and probably less important initiative was
going on at what was then called the Center for Drugs and Biologics, and
that was the erythromycin initiative. At some point in the early 1980s
in the first Reagan administration it was determined that in human
medicine erythromycin did not work for most of its uses, and therefore
since it was useless it should be taken off the market.

Then it dawned upon the decision makers and some committees that I was
involved with that to do that might not really be necessary, based on a
Royal Society of Medicine program that occurred in 1981 in London where
it was seen that following some hospital studies, some as long as a
10-year period, that if an antibiotic like erythromycin, or streptomycin
or whatever, stopped working then clinicians would stop ordering it. At
least they wouldnt order it at the same frequency. Then over time the
problem would sort of take care of itself, and to initiate action based
on antibiotic resistance had been shown to be problematically at best
and almost impossible based on the procedures that were in place at that
time.

So erythromycin remained, penicillin and tetracycline remained, and we
had one other seminal study by \-\-- from Holland which showed that they
could prove almost conclusively that the effectiveness of tetracycline
returned after a four-year period where it wasnt employed hardly at all
for any uses in that country.

Then, finally, I would comment more on the threshold document in this
initiative itself, but I think based on my experience in FDA and brushes
with the law, not only inside FDA, but elsewhere \--

(Laughter.)

DR. CRAWFORD: Were going to have to have something like this threshold
document. I do believe this is the beginning. In doing our Georgetown
study on antibiotic resistance earlier, about three years ago, I mean
when we started talking about whether or not people eat raw hamburger
and who eats it, and how much of it they eat and how much do they cook
it, and what do they put on it and how long does it stay in the
intestinal track, and so forth, and where did they buy it and what is
the \-- what is the real story about transmissibility. We ran into so
many variables that, I wouldnt say this in public, but Im not sure how
good our study was. Because we had so many variables that we just had
great difficulty dealing with it. If you see that paper and see the
charts that were there and the dead ends that the charts run into, it
looks very much like a childs drawings of about from a two-year old. I
think maybe thats kind of where we are, but the beginning is impressive
to me and also the availability of experts from industry, the consumer
movement and elsewhere to work together on this I think will give us
something which we have not had in the past, and thats quantification.
Because qualitative assessments and political speeches will not get this
job done. Thank you.

MODERATOR LATHERS: Thank you very much. I think we will now turn our
comment to Mary Bartholomew. Mary?

MS. BARTHOLOMEW: Okay. Id like to address the questions that have been
put before the panel, and the first question about \-- oops. Those are
the questions for yesterday afternoon, not to say that \-- okay. Thank
you. What are the strengths and limitations of the approach to
establishing thresholds outlined in the CVM discussion document? Let me
say that perhaps I might be a biased person to address that. However, I
am familiar with it, and I would say that despite our apparent
difficulties in conveying it the concept itself is really fairly
straightforward. Some have said simple. Given all the variables and
contingencies that have been discussed at this meeting what that
translates to is a facility in implementing the process of establishing
a threshold at least in so far as the process of calculation is
concerned.

So I think that given some of the comments that weve heard about how
rapidly can you implement this, all the while that youre sitting back
these bacteria are doing what theyre going to do. Can you act rapidly?
I would say that thats a strength, that this model is relatively
simple. There is in fact an awful lot of data that is required, and to
go out to all possible endpoints and all possible contingencies would
only add to the length of time it would take us to get something done.

The flip side of this apparent simplicity is that the approach is
perceived, may be perceived \-- in fact, I think Ive heard it perceived
as not addressing such concerns such as environmental reservoirs and
multi-drug resistance because theyre not written into the process per
se. My take on the approach is that by causing reduction in selection
pressure in many different \-- with respect to many different drugs,
that this approach will have a positive impact on these other concerns
as well. Because weve said that if the environment is a reservoir, if
we impact how we make selection pressure then were going to impact the
reservoir; and if were looking at many drugs in conjunction, that even
though were not tracking multi-drug resistance per se, we will have a
positive impact on reducing that.

So one of the limitations to this approach is not unique really to this
approach, and that is everyone is trying to broach this issue is and
will continue to be for quite some time hungry for more data and better
data, and a full understanding of the data that are available. Thats
what I would have to say about this question.

The second one, the second question is pertaining to what would we do if
the resistance threshold were calculated to be below the limits of
detection in the NARMS program, and if you look at how the resistance
threshold is calculated it appears to me that its mainly driven by how
we set the resistance thresholds. If we have this problem its going to
be not a sporadic problem. It would be kind of across the board, and it
would say something about the nature of the thresholds that we need to
set and the process. Wed have to rethink perhaps safety factors, are we
being to conservative with safety factors or something to that effect.
So naturally the conclusion that you would reach is if you calculate a
resistance threshold that is below the limit of detection, you would
have very \-- you would have a difficult time approving a product for
which that were the case. Because that means that if you were to let the
product out on the market, by the time you could detect the resistance
in the animal isolates technically speaking by nature of the way its
calculated you would have exceeded the human health threshold.

With respect to the third question, the outlined approach to the
establishment of resistance thresholds has multiple complexities. What
alternatives to this proposed approach should CVM consider? To date we
havent really heard them. So this afternoon should be a very
informative session for us, and we look forward greatly to hearing what
will be discussed this afternoon in terms of alternatives.

Number four is about how we might apportion the threshold, resistance
thresholds of different animal species, and in my talk I discussed the
uniform approach. That if you have four species, you would apportion it
equally. You would give equal weights, and what that amounts to is
saying that species which contributes most to disease would have to have
a lower level of resistance than other species.

An alternative that weve thought of is that you could apportion the
resistance threshold to the animal species according to the
contributions to the human disease that they make, and in doing so what
youd be essentially assuming is that the level of disease was partially
related to the amount of consumption of the product. Therefore, if the
product were being consumed more resulting in more disease, it would be
in some sense be more of a benefit in the food supply chain. So that if
you were to do this surprisingly what happens if you use proportional
contribution to the disease, the same level of resistance thresholds,
the same resistance threshold, would apply to all species. One of the
benefits of that would be it would just give us one number to be
monitoring and looking toward. That might be seen as a simplification.

How should resistance thresholds be updated after approval to take
account of new information, and what critter should trigger reassessment
of a current resistance threshold or lack of resistance threshold? In
terms of calculation, Im not entirely sure whether this question is
about how should we do in terms of calculation. That would be by the
same method that we did it in the first place. If this is about
procedures and policies, I dont feel very comfortable answering because
I dont know exactly in what form resistance thresholds are going to be
part of the approval in the first place. Its a thing I think is still
being worked out.

Then, what criteria should trigger reassessment? I think that we should
be open to multiple reasons for reassessing. One would be particularly
if the sponsor requested that it be reassessed because of major changes
in the way things are being done in terms of reducing bacterial load
overall. Overall, as has been mentioned earlier, if the bacterial load
were reduced then the exposure would be reduced, and then that would
have an impact on the rate at which you would approach the resistance
threshold. So there are some built-in safeguards in that respect.

MODERATOR LATHERS: Thank you, Mary. Linda?

DR. TOLLEFSON: I just have one general comment, and its really more of
an explanatory note. I think its very important to use the threshold
document not as a standalone document, but in conjunction with the very
detailed campylobacter risk assessment, the final version of which is on
our homepage. Many of the limitations that were addressed by some of the
presenters are really imbedded in that. Now, admittedly its only for
the issue of the direct health impact. That is the transfer of the
resistant pathogen from food to the human. However, we are in the
process of modeling an indirect transfer issue, that being enterococcus.
So that theoretically if that risk assessment is able to give us some
solid data, we would then be able to develop \-- go through the same
process for modeling the indirect transfer in, you know, developing a
K-res.

So again, I direct you to read the documents together. Many of the
complexities that are associated with any sort of a correlation between
whats in the animal food and the human health impact is really
addressed in the campylobacter risk assessment and not in the actual
threshold document thats in the back of the room. Thank you.

MODERATOR LATHERS: Thank you. Kim?

DR. THOMPSON: Yes. I guess I want to pick up a little bit on that point,
which is one of my \-- the spirit of my comment in some respects. Some
of my comments was that the \-\-- case is one case, and to generalize to
a much broader framework based on one case is probably something where I
wouldnt necessarily assume that all of the issues which were addressed
in the context of that case will be similar in their situations and in
the way that you think about them for all of the other cases that you
might encounter. For that reason, I would really encourage a lot of
additional consideration of a lot of these issues in the context of the
broader framework, even though you might feel that, you know, weve
already done a lot of that consideration in the context of that one
case. So I think thats one thing that I would put out on the table and
challenge you to think, you know, more broadly in the context of this
larger framework document.

Now, I think in the context of strengths and limitations strengths \--
first of all, were much farther than weve ever been in thinking about
this issue. We are all here talking about something that, you know,
people were not talking about five years ago. The campylobacter risk
analysis process has been going on for a while, but, you know, people
are here who are thinking about other pathogens, other antimicrobial
resistance issues, other products, other drugs.

So I think, you know, whats happening is that things are getting
broadened and the strength of the process is that you are operating in a
society which is highly litigious, which is governed by the rules of the
Administrative Procedures Act where agencies are required to do things
in certain ways, and you are effectively bringing people together to
talk about these highly complicated and sensitive issues. I think
thats, you know, a strength of the process, is getting people talking.
So I think thats an important and critical strength, and one that
should not be undervalued by you or by anybody else who is here.

I do think, you know, people have raised a lot of concerns about
possible limitations to the approach and that this goes back in part to
this idea of going from one case to a broad framework and whether or not
thats a reasonable extrapolation and what kind of issues you might want
to consider. I think the other challenge for those of us who model and
look a lot at what the mathematical relationships are is making sure
that a lot of the qualitative information, a lot of the issues that are
raised in the document itself, are also ones that find their way into
the actual modeling process if the quantitative model turns out to be a
very important factor that underlies decisions, which I think it does.

Okay. According to \-- I mean, the second question, I think the NARMS
program is a really important program and one that, you know, youve all
started, and its providing good information to you. I think thats
something thats a very important piece of this whole thing. I think one
of the critical things to think about is that you will find that
monitoring data are variable, and theres always a challenge when youre
doing monitoring to distinguish between whats a signal and whats
noise. You know, thats going to be a challenge here as well, and how do
you deal with the variability in the data. When is that indicating a,
quote, unquote, increase? When is that just sort of the way things tend
to go?

I think if you look at even some of the presentations that weve seen
you see that things go up and down a fair amount. So one question is how
do you deal with whether they are actually going up in a serious way or
whether or not theyre just kind of floating around some level that
might be okay. You know, how do deal with those kinds of issues is not
something that Ive seen yet really even been discussed.

Then the issue of being below the limit of detection. Thats always a
problem, and I think, you know, one of the things that you have to think
about in the spirit of my comment of is this special approach useful was
in part targeted around this issue of, well, you if you set a threshold
but you cant see it or you cant monitor it, then, you know, in those
contexts you better think about another

\-\--. Thats an obvious one, but, you know \-- so thats what I think
here.

VOICE: And of course we would say, Such as?

DR. THOMPSON: Well, I mean, I think thats something that you can
probably tell better than I. I mean, I cant tell you all here, but I
think youll have to. I mean, Im more than happy to help you think
about that, but I think \--

VOICE: Can you speak into the microphone?

DR. THOMPSON: Sorry. Im just saying Im more than happy to help think
about the next iteration, and I think that theres a lot of possibility
there.

Okay. Yes, there are a lot of complexities, and I think my strategy
would say think about where the models that you \-- where the model
doesnt work. Where it works and where it doesnt work, and make sure
you have the capacity to improve your model, to update your model. One
of the very difficult things about having a capacity in the system in
which we all work is that once things tend to get decided within our
political legal context they tend to be somewhat ingrained in stone, and
I think thats a big problem for all of us actually, and one that you
will all need to figure out how to deal with. So if you change the
thresholds, you dont want it to be perceived that you are changing the
thresholds in a way that makes things either less safe or more safe. You
know, you its youre changing the thresholds because youre doing the
right thing. You know, I think the whole issue of how people understand
what youre doing and how they think about what youre doing and how
that gets comminuted to the public and obviously the stakeholders is a
really important issue, and you should think a little bit more about
that.

Now, as far as dividing things up, I mean, that gets into a lot of
issues that are similar to ones that have emerged under the Equal
Quality Protection Act with respect to pesticides and the whole risk
concept for those who are familiar with that. I think there are a lot of
challenges here. I think, you know, you might find that there are
opportunities for discussions within the industries that are affected to
try to have them actually optimally find a situation.

For example, you know, you might try a permitting situation, like a
marketing type of approach, which might actually be better. Its hard to
know. So, I mean, I think some strategies there that might be considered
right ones, and, you know, it might depend on the extent to which the
various groups that are affected are willing to engage. So that puts
some responsibility on those groups as well, and I think thats one
thing to think about.

How should resistance thresholds be updated to take into account new
information? Well, I think the first issue is they absolutely must be.
This is a dynamic area. You have to have a process which is also
dynamic. One that is constantly looking, constantly surveying. Its
iterative. Its one where, you know, youre constantly asking the
questions over and over again. People are going to get sick of it, but
in that context I think, you know, its a situation where you have the
best chance of actually finding something and trying to intervene. I
think the challenge here is to figure out how to create effective
interventions in the regulatory structure in which were all working.
Thank you.

MODERATOR LATHERS: Thank you, Kim. Dan?

DR. SAHM: Okay. I will not address every single question, but picked a
few here that I feel most comfortable addressing. First it has to do
with what are the strengths and limitations of the document and the
proposal, threshold proposal. I do agree with the others that it is a
good start. The wheels are in motion to use such a process that gives
you perhaps some sort of quantitative measure. However, I have taken
actually a quote from what Kim used, and that is it really is too simple
at this point given the microbiological complexities; and I think that
the fact, another quote from her, is that although models are never
right, and even good models are never right, they can be useful. I do
agree with that, but I think we do have to get to the complexity of
these things. To that end, I think that Im a little bit familiar with
the campylobacter document, but I think that the complexity is such that
we might have to have a threshold model that varies with microbial drug
class and target organisms.

The other thing that I see that is limiting in the current document is
if the threshold is exceeded, what are the steps to resolution? I think
that with a model such as this, so simple that the threshold could be
exceeded because of background noise and not real events, and again to
Kims point, then I think when those thresholds are exceeded there needs
to be clear-cut strategies for delving deeper into the data and
information at hand to try and find out why the threshold, rather than a
knee jerk type of reaction that the drug would no longer be used. So
troubleshooting threshold breaches I think is another part that needs to
be more thoroughly developed.

Getting on to question number two, you never know in terms of
surveillance how much is enough. But I think one of the tactical things
that comes to mind is perhaps networking, bolstering the NARMS
initiative through private and public collaboration if possible. Also
networking NARMS with other surveillance initiatives that are going on
throughout the United States to collect more data and gate-keep that
information might be a possibility. Im not suggesting at this point
that I know that that kind of networking of other surveillance systems
would get the number up to enhance the sensitive of the detection
process, but at least as a tactical point of view there are other
surveillance initiatives out there that perhaps could be used to augment
the NARMS initiative and help the directors of the NARMS initiative to
gather more information from other sectors where they may not be getting
information at this point.

The other point that Id like to make is that I think that these models
can be challenged. I dont mean to insinuate that we have to wait for a
problem to emerge before we deal with it. Theres a number of large
surveillance initiatives that give us retrospective information that
challenges some of the suppositions of the model. Enterococcal
resistance being one that we could really start challenging the utility
of these models rather than going blindly into saying this is the
threshold model that were going to use, because I do think there is
enough retrospective data to challenge these models before theyre
actually enacted. Particularly the availability of data of course is
predicated on the drug-bug combination that is being considered.

The other thing, and maybe this was \-- pardon me if this has been
discussed already in the previous two days which I could not attend, but
it seems to me one aspect of this is to prevent drug resistance from
emerging in the animal population. Another is Im not quite sure whether
this model should take responsibility for or whether its handled in
another way, but what are the thresholds for measuring our ability to
prevent transfer of these organisms from food to the human population?
One is a therapeutic issue. The second one is a prevention issue
obviously, and Im not sure where the prevention issue has been delved
into.

Again, also finally as a general comment, I do understand the details a
little bit better of the campylobacter initiative. However, I think that
thats a unique \-- thats one unique situation and actually supports
the comment that drug organism combinations are unique in terms of
modeling. As microbiologists well know, campylobacter are intrinsically
resistant to nalidixic acid, a progenitor of quinolone. So in some ways
there are certain microbiologists that would tell you that
fluorquinolone resistance in campylobacter and the emergence of that was
almost predictable prior to its use. Thats not necessarily true with
other drug-bug combinations. So Im just again, to make the point, and I
guess to beat a dead horse or a dead beef or whatever, the drug-organism
combinations are unique enough that a broad-reaching applicable model
does not seem necessarily useful.

MODERATOR LATHERS: Thank you. Rob?

DR. TAUXE: To speak first a bit about strengths and limitations of the
approach, I think a great \-- there is a great virtue in simplicity and
that to have a working model of direct harm in front of us and available
for really making concrete is tremendous, and I think that although
there is a suggestion, one of them mine and from others, that there are
several different harms and that ultimately perhaps even different
models may be appropriate for different kinds of harms. I think that
its important to go forward with a relatively simple and comprehensible
model that basically is a new agent X used in a single species or
perhaps a couple of animal species as a model for us to really work
through. So I think that the simplicity is actually a great strength at
this point.

Surveillance is monitoring linked to action. Theres a lot of monitoring
that happens, but if it is not linked to action in some way I cant call
it surveillance, and making that linkage, formalizing that linkage so
that action begins to occur as a result of monitoring is critical. Some
of that will be informal action or voluntary action. I think the
essential concept here is that there is a mandatory action that happens
at some point which we hope would trigger all sorts of other prevention
measures that would be taken, that would come into play earlier and
perhaps mitigate the development of resistance enough that there is no
final withdrawal.

I think that therefore we have a conceptual challenge first with the
threshold in that as we actually are looking at a continuum of
resistance we hope that there is a continuum of interventions that
occur, and that to hang the absolute threshold and link it to the
mandatory withdrawal of a drug has focused everyones attention on that
specific event moment and level while actually there is a lot more that
we hope would be going on. I think that another strength besides having
explicit triggers to action is that a straightforward approach for
having a new drug be considered would be a tremendous help to everyone
in the room, and that being able to map out exactly what a model would
look like would be a very great benefit.

As far as the weaknesses, I think that the considerations that there are
perhaps different kinds of harms and different kinds of models, or even
that we might need a salmonella model and a campylobacter model, and
perhaps a separate enterococcus model leading to a proliferation of
models is going to be a challenge. I would hope that we could hook
together a common consensus on the human clinical impact measures that
could be used and use those to link them together and make them
consistent.

The second question, according to the approach outlined resistance
thresholds are derived using an epidemiology-based model, and I guess
the chief critique weve heard of those is that theyre a simple linear
model of what may well be a non-linear situation. Unless we know what
the shape of that curve is, its going to be hard. I find it a little
hard to say what alternative curve should be substituted for that. It
reminds me a little bit of the discussions of sort of limit dilution
exercises with toxicology where you have to extrapolate somehow, and a
linear extrapolation is one way to do that. So there may be alternate
curves that could be substituted. I think wed have to see why a
different curve than linear, would \-- one would be better than another.

I think that the question of whether we would detect the change or
detect the detectability of the thresholds that were talking about is a
real important one. I would assume that if were operating in the range
of limiting human impacts to something on the order of 10^-4^ per
illness sustained in the human population then that would translate into
resistance levels for the \-- among human isolates of between 0.1
percent and 10 percent just as a rough range, being that that would be a
zone of where the highest concern would be located and that that would
mean similarly in pathogens isolated from foods or food animals we would
be operating in approximately the same area of the graph. I think that
with some modest expansions I think that the system is able to establish
resistance thresholds that would be \-- is able to measure resistance at
that level.

As far as attributing to multiple food species, when use has occurred in
multiple food animal species I think thats going to be a challenge that
depends \-- a great deal of that depends on our ability to allocate the
risk of the consumption. The risk of, say, something like salmonella or
campylobacter across the different food commodities. As I mentioned, I
think that there are a number of standing approaches, and there may be
some new approaches to do that based on detailed subtyping of strains
that may even be applicable to the resistance genes themselves that may
help firm up the attributable risk calculations. I think that it could
be done.

The further we get from human harm, the more we are constructing a model
that will have error bars around it and the greater desire that we all
have to use that as early warning. So I think our challenge is to use
thresholds in foods or food animals to \-- that will predict the harm,
but recognizing that our measures become more and more imprecise the
farther we get from human harm.

Finally, the question of updating. I certainly agree with other speakers
that thats going to be very important to have an update cycle somehow
built into this. For me, things that would challenge the resistance
threshold concept would include a major change in the transmissibility
of organisms through food. For example, if we began to irradiate major
commodities of food, that might perhaps change the overall human health
impact of pathogens transmitted through that commodity, including the
resistance ones. I presume that would really change K, the K function,
substantially. So that might be an example of when things might be
reassessed.

MODERATOR LATHERS: Thank you very much. Marc?

DR. LIPSITCH: Can I make a point using one of my slides?

MODERATOR LATHERS: Yes, you may. Why dont you hook that right in?

DR. LIPSITCH: I want to make points that mostly address the first and
last questions and will hit some of the others in between. But just as a
preliminary point, I think any \-- weve talked a lot about
uncertainties and inaccuracies in the model, and Im going to talk about
some points of the same sort in a second, but I think its important to
remember that any physician who has ever treated an acutely-ill patient,
or any vet in the same situation for that matter, knows that you cant
wait until you know everything before making a decision.

I think in public health, which is what this is all about, the same
thing is true. You dont want to be ignorant of the science, and you
dont want to be ignorant of what your problem is that youre dealing
with, but the point here is to decide how antimicrobials should be used
in animals. Its not to produce a journal article or to produce a piece
of science which is totally unassailable. The science is a tool, and
its not the endpoint.

Having said that, I want to make a few scientific points that I think
might be useful in trying to improve the threshold approach, and Im
really echoing what has been said by almost every other speaker here,
that the drug and the organism really make a difference to how you might
set the threshold.

(Slide.)

What I have here is a diagram of \-- well, youll see how its
developed. So there are bacteria in animals, and one effect of
antibiotics may be to create or accurately to select the rare genetic
event of the creation of the resistant organism that wasnt there. So,
for example, what was referred to earlier, the transmission of
resistance between \-- of streptogramin resistance between different
species of enterococcus might happen in an animal, and antibiotics might
select for that. So thats one way in which antibiotics could be
ultimately linked to human resistance. Another way is that they could
just amplify something thats already there. So there is
vancomycin-resistant \-- well, there is fluoroquinolone resistance in
campylobacter, and treatment of animals can amplify that.

That can then lead to resistant bacteria in food, which in certain cases
like campylobacter that weve heard about, or salmonella, can lead to
resistant human disease, and I think that for all its shortcomings the
rather simple model thats been proposed here is pretty good for that
sort of thing where there is a measurable association and a quantifiable
association between disease in people and its source sometimes in the
food they eat.

But there are other more indirect methods \-- links as well, which we
know these links exist, but quantifying them is a little bit more
difficult. So, for example, and these have been mentioned as well today,
resistant bacteria in food such as e. coli or enterococcus can become
resistant human commensals. So at that point the transmission has
occurred, but quantifying it is going to be hard because nobody goes and
samples and extensively peoples commensal flora, except in very rare
circumstances. In opportunistic cases that may become a pathogen for
those people, but, again, seeing the link and quantifying the link is
going to be difficult.

It gets harder when you realize that that person may not be the one who
gets sick. So there may be transfer-resistant organ \-- resistance
determinants to other organisms or transmission of those bacteria from
human to human, such as e. coli for example, and then those people may
get sick. So drawing the link back from that yellow, resistant human
disease all the way back to the antibiotic use in animals in a
quantitative way, or even by strain typing, is very likely to be almost
impossible. Yet, we know that each of these processes happen. So at some
rate probably there is such a link.

(Slide.)

So I think there are really three kinds of harms that we can think about
which are different, which apply to different organisms, and different
kinds of thresholds might be appropriate. The ones where the food
disease link is quantifiable as in the campylobacter case. The ones
where the food and resistance link is probably identifiable perhaps by
strain tying, for example where the individual ate the food and later
gets the resistant infection. But its going to be difficult, and its
certainly not quantifiable. The third is going to be a category of harm
where were just never going to be able to do any interesting science on
it on the direct link from the resistant human disease in someone who
didnt eat the food but was exposed to someone who did. We know those
steps occur, but were never going to be able to draw that link, and
theres no point in trying.

(Slide.)

So I think the human health threshold thats been described where we
look for resistance in people and try to attribute that back to the farm
is appropriate for foodborne organisms that are primarily human
pathogens, like campylobacter and salmonella, and that the prevalence of
resistance can play a confirmatory role.

(Slide.)

I will just summarize what I was going to say, which is that in
organisms like e. coli and enterococcus where the harm is more indirect,
the microbiologic threshold is going to be a more appropriate way. I
want to close by saying that I think revisiting this point about
uncertainty as an academic I have lots of points which Im not going to
get to about why the \-- how the things could be changed and improved a
little bit, but as a citizen and as a \-- if I were a care giver, I
would want the FDA to make decisions that protect human health,
regardless of how certain we can be.

I am going to close with a little story that I think relates \--
summarizes the kinds of issues that we face at this point. Its about
someone who wanted to know whether the little light inside the
refrigerator goes off when you close the door. So their first experiment
was to push the little button that gets pushed when the door closes and
noted that the light goes off. The sceptic says, Well, but how do you
know that thats the same thing that happens when the door closes? So
he does another experiment. He carves a little window in the
refrigerator door and closes the door and sees that the light goes off.
Then the sceptic says, Well, how do you know that you havent changed
things by carving the window? So he puts a little child inside and
says, Tell me if the light goes off, and the sceptic says, Well, how
do you know the child was telling the truth?

You know, there is a level of certainty at every \-- there is a high
level of certainty that we can all aspire to as scientists, but that
level of certainty is not going to be achievable in practice in every
case. I think thats a balance thats important to follow. Thank you.

MODERATOR LATHERS: Thank you. We will now entertain questions from the
floor for members of the panel. Microphone to my right.

DR. KEELING: Im getting up early this time so we dont run out of time.
Im John Keeling with the Animal Health Institute, and I agree with Dr.
Tollefsons statement that you really do have to look at the risk
assessment and the threshold document both to really get an
understanding, because many of the concepts there or the ideas in the
threshold document really stem directly from the assumptions in the risk
assessment. In that vein, I think its unfortunate that the agency
wasnt \-- didnt or wasnt able to get Dr. Vose to be at this meeting
to be able to talk more about the risk assessments since he was the
developer of it. But my question really goes to the issue of the
relationship between what the linear relationship which has been talked
about some more before, the idea that H is equal to K and Q, to some
function of those. It unfortunately takes me back to seventh grade
algebra, and I think of that original linear equation that we all had to
look at where Y is equal to A(x) plus B. So I start to think about the
components of that and then ask questions about this.

My questions really will go Drs. Bartholomew and Thompson really,
because I think whether or not we can determine whether or not there is
a better relationship, a non-linear relationship that describes that is
a real question and one that needs to be answered. But since this model
does use a linear relationship I think we have to be clear about what
the implication from that linear relationship might be, and in the case
of the K factor which is then linear, that gives us a certain shape to
that curve. That curve defines kind of the policy options that are
available in terms of managing or changing Q. In other words, the shape
and the slope of that curve gives you an area underneath there that as
you change Q determines what the change in H is, which is H is the
object that were really concerned about. So if that is in fact
non-linear, you know, that curve changes and our responses get \-- the
management decisions we make will get different rates of success in
lowering H. So Id like to hear your response to that and how that would
impact the thing.

Then an additional, then I look at that Y as equal to A(x) plus B
equation, and I think about, you know, graphing. Picking those sets of
numbers and then laying them out there on the line and seeing them all
go, and I think about what does B mean. Well, B was the thing that told
us whether or not our line went through zero or not, and in this case it
seems us that B tells us all of those baseline factors or other causal
factors that may not be accounted for in K. If we dont have that number
and its assumed to be zero, that would tell us that every time this
line is going to pass through the origin I assume, and if it passes
through the origin every time then it raises the question in mind that
if you were to pick a random set of variables, generate a random set of
variables, how would that line pass through them given it would be
constrained to go through zero?

Or in fact, if the variables that you laid out in that upper right
quadrant of your XY axis were all configured in a way that would lead
you to believe that there was a downward slope. Yet then you took this
H=KQ function and laid that across those points, what would you
encounter? So my questions to you is given the varying data points that
might be out there and looking at randomly generated ones, are you
comfortable with the assumption that no other causal factors are
available and so therefore this zero intercept and essentially a
constant determination between those points is credible?

MS. BARTHOLOMEW: Okay. Well, when people talk about non-linearity in the
relationship Im not sure whether weve got our Xs and Ys lined up.
Were talking about in that graph K-res is the relationship between the
number of cases, so on and so forth. Then Q is the quantity. Now what
weve shown on the graph in figure four is that relationship once we
have assumed a constant volume of consumption and a constant carriage of
bacteria on the \-- in the meat. So that what were looking at is now
the relationship of h, the prevalence of resistance in the isolates and
the resistant cases, H. So that Q is not what you actually see graphed
there.

Now its true that if you go back to the original equation youve also
got the linear relationship, but the (0,0) is applying to the number of
cases who would have a resistant disease if all of the bacteria in the
meat supply were resistant \-- or I should say none. So if you have no
resistance in the bacterial isolates from the commodity, you would have
none of the cases that were attributable to that commodity with
resistance either. Thats where that (0,0) comes from. So Q, if you want
to look at the relationship and graph the relationship with Q, then
thats going to be somewhat a different story, because you have now the
impact of the volume of consumption and the carriage rate so that the
axis is not as in Figure 4. You would still have none resistant, yes.
But in the graph in Figure 4 (0,0) relates to the percent resistant in
the isolates.

So that linearity is assumed just a relationship of resistance in the
animal species, resistance in those human cases that are attributable to
the animal species. As we see it, non-linearity that people are
concerned about would relate \-- would come into that model through
trying to incorporate resistance in the human cases that are no longer
attributable to that source but through perhaps application by human use
of the drug \-- or from travel. But if we had domestically-acquired
cases and domestic production then those things would relate linearly.

Now, it may be that that is a simplification. We think it is likely to
hold under conditions of stable consumption and carriage rates. But
thats why were building in the potential to come back and look at it
on a regular basis in case there is something thats not picked up in
there, and in fact in case things are changing that would also be
reflected in K-res.

DR. KEELING: Thanks, I think.

MODERATOR LATHERS: The gentleman at the middle microphone. Please
identify yourself.

DR. THOMPSON: I want to actually \--

MODERATOR LATHERS: Kim?

DR. THOMPSON: Would it be okay if I pick up on that? I mean, I think
thats an excellent question that was raised. There are a lot of
problems at the 00 point. So if the equation is the small t equals large
T over zero it doesnt really matter what large T is. You are dividing
by zero and youre multiplying by zero. So you can run into this
argument of some people saying that to divide by zero means that the
threshold is infinity, and other people saying that to multiply by zero
means that the threshold should be zero. I think you can run into a lot
of problems at the zero point mathematically, and \--

MS. BARTHOLOMEW: Its a construct to get the graph so that you can hit
the point. Yes.

DR. THOMPSON: Right. But Im just trying to say, you know, one of the
things you look at with respect to any model is how does this behave in
a limit. So, you know, theres some challenges there to still think
about whether or not, you know, this is making sense as you approach
zero, or some other situations might be problematic.

I mean, I think the other challenge is when we think about the NARMS
data and we think about when we test drugs for the efficacy originally
in humans theyre typically not 100 percent effective. So there might be
some theory that you test a drug and its not 100 percent effective. So
theres already either some resistance or theres some other thing that
makes the drug not 100 percent effective. So what does that mean? Well,
it might mean that there is some background and that thats something
that may need to get factored into the model.

So I think one of the things that, you know, you might want to raise
then in the context of this \-- particularly I think the real challenge
is going to be, you know, when you start using the monitoring system for
the surveillance process and you start running into a lot of the
variability thats going to happen in the data. I mean, thats just
predictable from what we know about how monitoring works. There will be
a lot of challenges in trying to find out whats background, whats not
background. You know, where should we be heading. You know, I think, you
know, in a context of developing this framework thats why I think this
is a great start. Because I think youre going to learn a lot as you try
to apply that, and that youre going to have to make some changes to
adapt to what you learn.

MODERATOR LATHERS: Thank you, Kim. The gentleman at the middle
microphone, please.

DR. DOWERSON: Mike Dowerson, Toxicology Excellence For Risk Assessment.
I have two questions. Ill ask one and then Ill go to the back of the
line.

MODERATOR LATHERS: Thank you.

DR. DOWERSON: Lots of people here. The question is a continuation to Dr.
Bartholomew. I was confused with figure four because I looked at it and
I did not see the slope of the line being K-res. I worked out different
units for K-res. Youve given an answer I think that helps me to go back
and try to reinterpret that. So I think Ill be able to handle that, but
my question goes into model validation and model verification. We all
mean different things by that, but the definitions I use are perhaps
more common. Validation is you take a model and you put reasonable
numbers in and you see how it works, does it work in an expected
direction. Then verification would be does it reflect reality. So my
question is now on model validation. I put reasonable numbers in and due
to limitations to myself no doubt, because Im a tox person, I got out
values of t(x), small t(x), that were greater than one.

Now, it might be due to the fact that maybe values of t(x) should be
greater than one, but I thought it was a proportion that should always
be, you know, between zero and one. So my suggestion or question is did
you do model validation, and if you could maybe in your appendix example
go through your calculation as you did and then put in the calculation
of small t(x), which I didnt see there. Maybe I missed it. That would
really help.

MS. BARTHOLOMEW: Right. And one difficulty in our doing that is that in
order to calculate little t(x) you have to specify a capital T(x), and
that was one of the basis \-- one of the things that we hoped to discuss
at this workshop, is how would big T(x) be set. So you might go through
and pick what you might think would be t(x), a capital T(x), that you
would like to see in place and see what kind of little t(x) you get if
you plug that in.

DR. DOWERSON: Okay. Well, I just did one. I picked a T(x) of \-- a big
T(x) of 0.01, just to pick one, and I went through and I used some other
values that are probably unreasonable. I kept getting a small t(x) of
100.

MS. BARTHOLOMEW: And the problem is that your units on the big T \-- the
big T if you are doing in terms of a probability you are going to have
to apply that and say what are the number of people that that \-- in the
population that that implies and plug in that sort of number, a number
of people.

DR. DOWERSON: Okay. Thank you.

MODERATOR LATHERS: Thank you, Mary. The gentleman to my left.

DR. NORTON: Yes. Bob Norton from Auburn University. My questions or
comments are directed to Mary Bartholomew and Kim Thompson, and, Kim,
let me first congratulate you. I think youve probably given us the
take-home quote for the day with A model should be as simple as
possible, but no simpler. I think thats a great quote, and Im sure
were going to hear it again.

MS. BARTHOLOMEW: Could I ask you to speak up a little bit, please?

DR. NORTON: Certainly. This is first of all \-- let me direct this to
Mary Bartholomew. You stated that K absorbs the various things that
happen. That it contains a certain number of immuno-compromised
individuals and it uses a population average approach. Then you said
that there would be monitoring to pick up seasonal trends, and, Kim, you
stated that H(x) should depend on pathogenicity, virulence, hygiene
practices, et cetera, et cetera. It strikes me that what is consistently
being implied here is that the model really fails to consider the
inclusion of confounders, risk factors, and it seems to me that those
would be very critical to the assessment of the model. Id just like
your comments on those, whether these should be included. Maybe it
should be rewritten and these be included.

MODERATOR LATHERS: Fine. Kim?

DR. THOMPSON: I mean, I think that the critical thing with modeling is
that you want to make sure that when you push on something that the
thing that you expect to have happen happens. So if there are critical
covariants that are not in the model and they either moderate or
otherwise impact what happens when you try to manage risks so that what
you expect to happen doesnt happen then thats a huge problem, and it
just means that your model is just not right.

So I think that to the extent that there are covariants that can be
identified that need to be built into the model that that needs to
happen. I mean, I think that thats an important next step, and I think
that, you know, its \-- one of the real challenges here is that the
devil is in the details. I mean, it was clear to me in my reading from
the document that the CVM is aware of all of the complexities that
exist, but its a challenge to incorporate all of it into a model.
Particularly its a big challenge to incorporate all of it into a
framework model that is supposed to kind of possibly apply to
everything. Where in fact there might be certain situations where
something is irrelevant and other situations where other things are
relevant; and in the context of developing the simplest model that
worked, but isnt too simple, different things might drop out.

So, I mean, I think thats one of the big challenges here, is to think
about how is it that we get the covariants that matter into the model or
models, and how is it that we keep them from distracting us in
situations where they dont matter. So, I mean, I think thats a big
challenge, and I appreciate your comment.

MODERATOR LATHERS: Thank you, Kim, for your comments. The gentleman to
my right.

DR. WAGES: My name is Dennis Wages and Im a professor of poultry health
management at the College of Veterinary Medicine, North Carolina State
University. My question is for Dr. Tollefson. If and when the threshold
line is drawn, and you commented on the regulatory actions that would
take place and there could be a variety of mitigation strategies
implemented by companies, industry, whatever to that, but if the line
gets drawn and based on NARMS data that threshold gets \-- is crossed,
and the predominance of the data is based on chickens, and we dont have
the information that the turkeys have crossed that threshold, or vice
versa. We could find that the chickens are clicking along and the
turkeys have already crossed it. Is there a regulatory mechanism in
place that could separate \-- the approval is, you know, for chickens
and turkeys. We lump poultry together, but there is clearly \-- turkeys
arent chickens and arent table egg layers. Is there a mechanism to
separate those two on the potential withdrawal of a product and the
second kind of dovetailing that if in fact a company through
irradiation, cooked product, through their judicious use of
antimicrobials and through HACCP sampling, whole bird washes, they
clearly are not approaching this NARMS industry threshold, i.e., the bad
apple spoiling the whole bunch for the industry, is there a mechanism in
place to allow selected \-- that could be a regulatory nightmare, but
use of product much like in hospitals where certain classes of, not
maybe antimicrobials, but are restricted to teaching hospitals, et
cetera?

DR. TOLLEFSON: Okay. In answer to the first question, we would have
regulatory options based on whatever the label is approved. So whatever
use in the NADA, thats where we would be able to take regulatory
actions. So in your first example, we could remove it from \-- we could
remove from the label approval in turkeys, keep it in chicken and cattle
or companion animals, whatever.

The second question is a little bit more problematic, because NARMS is a
national surveillance system and were working with the U.S. populations
as a whole, both on the human side and on the animal side. Its really
the food supply as a whole. So, no, we would not be able to. We would
not be able to draw the distinctions. It would sort of be like a
permission or a permitting type thing. No.

MODERATOR LATHERS: Thank you, Linda. Center microphone?

DR. GINOVIN: Yes. Im Michael Ginovin, M.E. Ginovin and Associates. Dr.
Thompson unfortunately stole my opening remark. The devil is in the
details. I think as an example Ill take the slide up there. It says,
The maximum allowable prevalence of resistant bacteria isolated from
animal-derived food. Well, the phrase that is troubling is isolated
from animal-derived food, because it implies a sampling paradigm. It
implies that were doing something in a specific way, and having planned
a number of sampling plans in my career I can tell you that how you
sample influences very greatly the answer you get.

I think in a similar vein were talking about attribution of disease by
source of that disease. Now, anybody who has gone through the exercise
of disease attribution knows that once you have identified all your
sources and attributed a fraction of the disease caused by those
sources, you have a good deal more disease than actually occurs. So you
have to do something about that, and sometimes you end up with the part
that is least well known getting a large fraction by simple fact of
ignorance.

Now, I think the other issue is the assumption that were going to be
able apportion disease by the level of resistance in bacterial which
were isolating pretty far back in the process. If I have, for example, a
manufacturing process which is relatively pristine and its putting out
a very high quality, very low contamination product, I have less of a
problem than the guy down the road who may have a less pristine
facility. Anybody, again, who has looked at industries and says, Where
does most of the contamination come from? Most of the contamination
comes from the 35 percent, and there are a bunch of questions out there.

I guess my closing remark is that someone yesterday or the day before
mentioned that we should try dry-labbing some of these regulatory
processes. I think thats a very good idea, because Im hearing that
were trying to discuss this at a very, very high level of extraction,
and people keep coming up and saying, Well, I dont quite get it. I
dont understand. Im getting a number greater than one. I think the
problem is that we may be trying to discuss this whole problem at a
level of abstraction which is really more abstract than is useful for
really coming to decisions. Thank you.

MODERATOR LATHERS: Thank you for your comment. The gentleman to the
right.

DR. WILSON: Jim Wilson for Resources for the Future. I have a question
for Dr. Lipsitch, and pardon me if I mispronounced your name. Im
working on an idea that the use of this linear relationship, this direct
proportionality between one prevalence and another is not too simple,
but that it adds an unnecessary complication to what might be a simpler
approach to this problem. Dr. Lipsitch showed a couple of data sets that
suggested that the relationship between use of the antibiotic and
appearance of resistance was time dependent, and maybe these were both
reductions over time and that there was a time lag. But it also, at
least in the cases that he showed, it was a pretty clear \-- it showed
pretty clearly that the relationship between the usage and the
prevalence of resistance was not constant with time. That one was going
down and the other was going up.

If thats the case and since the data that you are actually going to be
looking at represent time series rather than individual data points, I
mean, that begins to suggest a regulatory approach where what you
monitor is not a point a year, but the change from year to year in
whatever variables seem appropriate. Now, did I understand your data
correctly?

DR. LIPSITCH: I think so, and I think its \-- that thats an important
point, that the process of \-- that determines what level of resistance
there is in a population is a birth and death process, and its a
selective process. So the amount of antibiotic use wont necessarily
determine how much resistance there is this year, but rather it will
determine the direction of selection. If theres a lot of antibiotic
use, all other things being equal, there will be an upward trend. So
its the change I think as you mentioned thats important in the
prevalence of resistant.

DR. WILSON: There are mathematically generally comparable situations
where the change in resistance wouldnt be linear, but would be
super-linear, perhaps even exponential in that a 10 percent increase
might lead to a 40 percent change in the resistance three or four years
down the road. So use of this linear proportion as a way to justify
action based really on change in resistance in the animal population
really may just be adding a complication that confuses everybody without
adding anything to the regulatory situation.

DR. LIPSITCH: But linear was a different point, though, I think. The
linear was not a relationship between antimicrobial use and resistance,
which I agree will not be linear in any easy sense probably, but between
resistance in animals and resistance in humans. I think there are other
reasons to think that may not be linear, but I think its a different
point from the one youve just made.

DR. WILSON: Okay. Thanks.

MODERATOR LATHERS: Thank you. Center microphone?

DR. VOGEL: Im Lyle Vogel with the American Veterinary Medical
Association. Years ago when I was studying for my masters degree at the
University of Minnesota School of Public Health I had a heavy emphasis
in epidemiology and biostatistics, and I was taught by some of the
world-renowned experts in those areas. I mention that because with that
background I expected to be able to go into the threshold or the
discussion draft of an epidemiologically-based approach to establishing
resistance thresholds and they would be understandable. Unfortunately, I
need to admit that Im maybe on the same level of understanding as
Richard Wood admitted the other day.

One of the cardinal rules that I learned in epidemiology is that you
always at least address, if not control, confounders and other risk
factors, and this document does not do that. As I understand it, it
attempts to do that by rolling that into the K-res factor, which is then
a proportion derived from a risk assessment and then is treated as a
constant to predict what \-- as measurements in animal resistance, what
effect that will have on human health impact. It just doesnt work,
because theres too much biological variability that is rolled into that
factor.

We were advised to take a look at the campylobacter risk assessment for
more detail in applying this model, and Ive done that. When the FDA
updated the campylobacter risk assessment model from 98 to 99 data,
theY incorporated new chicken consumption data. That chicken consumption
data was an increase of about seven percent. Thats not a minor change,
and that seven percent increase is largely responsible for the reported
increase in human health impact numbers from 5,000 up to 9,000.

But the problem with that K-res and that seven percent increase in
chicken consumption is that the risk assessment is treating all chicken
consumption as equal, and in fact not all chicken consumption is equal.
The CDC case control study shows that theres much larger risk from
chicken consumed in restaurants, and in fact chicken consumed at home
has a protective factor and chicken eaten in fast food restaurants has a
much lower risk factor.

So when you combine, average all that out, and you combine it into the
K-res factor you are hiding some confounders and some other risk factors
that are having a great or potentially can have a great consequence on
veterinary medicine, and I dont think thats right. Thank you. Anyone
care to address that?

MODERATOR LATHERS: Mary?

MS. BARTHOLOMEW: Well, I do agree that K-res has a lot in it, and I
think that given the nature of what were trying to associate were not
trying to say for this person over here, you know, hes got a certain
sort of risk, and for this one over here hes got a different risk.
Were not doing person-to-person assignment of risk. Were saying that a
certain amount of contaminated product leads to a certain amount of
cases with resistance that we attribute to the animal source, and were
doing the attribution through \-- well, I should say that the
attribution is the resistance through subtracting out resistance that is
due to travelers and thats due to people having received the drug of
concern prior to being sampled.

So I see those confounder variables that youre talking about being very
important if I want to try to assign risk to subsets or to individuals
in the population. I agree that there might be some impact of them on
the variation. If you have confounders then you can account for a
variation, but I do not believe that going into all that is going to
give us a better means of being able to draw this association and try to
develop a basic threshold.

DR. VOGEL: I think I disagree, because if more people eat chicken at
home as compared to your baseline as opposed to eating chicken in a
restaurant, that can make a big difference in your end number. You are
making in this estimation, youre making an assumption that those things
remain stable over your period of interest, and that may not

\-- thats a big assumption, and it could be a false assumption.

MS. BARTHOLOMEW: It gives a basis for setting a little t(x), and then we
are monitoring and that would have an impact then on the number of cases
who wind up in that h(x). So thats really where its going to show up.
All the things that as I think someone on the panel earlier said, is
that, yes, we want to be monitoring and doing surveillance on the rate
of resistance in the isolates. But were hoping that at the same time
all these other measures are being put in place and will have the
ultimate impact of \--

DR. VOGEL: But if youre taking action based upon little t(x) that is
calculated incorrectly, those other mitigation factors, such as cooking,
changing in consumption patterns, is not going to solve the problem.

MODERATOR LATHERS: Linda?

DR. TOLLEFSON: Id like to say something, too, that I think were losing
sight of, and that is there are also many aspects of attributable risk
that we did not take into account. For example, we did not model the
effect of cross-contamination from the chicken. People who dont consume
chicken could have ingested the campylobacter from their salad, but it
really came from the chicken. We didnt do that, and part of the reason
we didnt do it is because the more you refine it then the more
uncertainty youre going to have to model in also. If we put in all
these pieces of information, were going to end up with a little t(x)
with huge safety factors so that itll be worthless. The better that
were \-- the more simple we make it, the better off we are.

DR. THOMPSON: Could I speak about that?

MODERATOR LATHERS: Kim?

DR. THOMPSON: Yes. I mean, I think theres a real challenge here. If you
have a model for which the risk management strategies that are open to
you make no difference then theres a problem with that, because then it
means that people might \-- well, first of all, I mean, I know. Im just
trying to expand on his point, which I think is at the crux of this
comment. Which is that people do do things that moderate or increase
their risk, and that may or may not have an impact on how you regulate.
Okay? But it does have an impact on peoples risk level.

So how you model risk makes a difference in how people perceive the risk
and how they take action, and I think that the challenge here is that
you need to have enough consideration of the uncertainties so that you
are looking for better information about mechanisms, about what people
can do and what they do do, and create the incentives for people to be
actually collecting better data.

At the same time, you cant just throw up your hands and say, There are
just so many uncertainties we dont know, and then resort to the
position of, Well, science tells us nothing. Right? Because thats not
going to be helpful, either. So I think the challenge here is to walk
the fine line balancing what it is you know, what it is you need to
know, and what it is that you can confidently make decisions about now
and how you need to get yourself to a position where you can confidently
make decisions that you are going to need to make later.

I think the challenge with a very simple model is that people cant see
how the actions lead to changes that reduce risk. Now, as I said before,
a lot of those issues are discussed in the document. Its clear to me
that you and other people understand all the complexities. Now, modeling
them is a bigger challenge, and I think thats where, you know, things
have to go.

MODERATOR LATHERS: Okay. Thank you, Kim. Microphone to my right. Dr.
Cox.

VOICE: We have one more. Marc.

MODERATOR LATHERS: Marc? Okay. Im sorry.

DR. LIPSITCH: If I could respond briefly. I just want to respond briefly
to the previous question also. I agree with virtually the entire premise
of that question, and thats a reason that I would advocate as a
strategy, and I started to say this but got a little bit too wordy about
it in my last comments, I would suggest that for something like
campylobacter or other \-- another human \-\-- human pathogen \-- I
would ignore to a first approximation \-- nearly ignore the data from
the meat, and I would just look for attributable \-- for
meat-attributable resistant cases in people, and that can be done.

You get a denominator from total burden of disease and you get a
fraction from case control studies, and you dont have to look at
exposure directly. I would say that in those cases you can actually
simplify, get rid of the whole K-res, which I agree is full of problems,
and just look at attributal human cases.

A further advantage of that is that it provides an

incentive for what the gentleman over here mentioned, which is that
people \-- that there is an incentive to use better practices to prevent
contamination of meat in general. Because if you do that, there are
fewer cases, there are fewer resistant cases, and you move back from the
threshold if you just look at number of attributable resistant cases in
people. Thats what I would advocate.

MODERATOR LATHERS: We are looking at the time. Its 12:15. I think we
should adjourn now. For those of you who have further questions, and
there are a number of you at the microphone, during the public comment
period we will begin with your questions if you will again line up at
the microphone. We will now adjourn for lunch and begin at 1:15. We will
see you then.

(Whereupon, a luncheon recess was taken at

12:15 p.m.)

[A F T E R N O O N S E S S I O N]{.underline}

(1:30 p.m.)

***[Public Comment Period]{.underline}***

***Facilitated by Jim Heslin***

FACILITATOR HESLIN: We are about to begin the public comment session,
which was originally scheduled to run from 1:00 to 2:30. I would still
like to look at 2:30 as the ending time if possible, but we dont want
to cut anybody off. Well extend it if necessary, keeping in mind that
there is another public comment session at the end of the day.

I also understand that some of the individuals who had some questions or
comments as part of the discussion session earlier couldnt get their
comments in. So if you want to use this opportunity as well to address
questions for discussion purposes Im not sure that all the panel
members have remained, but there are some of them in the front row here.

Ground rules are the same as yesterday in terms of the public comment
session. Limit your comments to five minutes. I will give you a
one-minute warning. Identify yourself and organization you represent,
and well start in the middle.

DR. DOWERSON: Mike Dowerson, Toxicology Excellence for Risk Assessment.
Actually this is a question picking up from the previous commentary, and
its a question to Dr. Tauxe. I hope Im pronouncing your name right.
Its picked up on an observation that was made by one of the other
presenters, Dr. Thompson, in that she had difficulty in figure four
establishing the baseline along the Y axis, the capital T~x~. Now where
is that, and how should we go about it? So then on the basis of that
inquiry, which I very much felt the same way, the question is to Dr.
Tauxe or maybe some other clinicians, and maybe even some veterinarians
whove had this kind of experience. The question is what type of percent
resistance in your clinical practice would start to cause you to have
concern, and can you categorize the percentages across the board or with
certain types of bacteria into one of several categories? And the
categories might be a threshold in the sense of how capital T~x~ has
been defined. Its a threshold, but now you think theres going to be
human harm at a certain percent resistance, a higher percentage where
you are beyond that threshold and youre into some sort of emergency
behavior, whatever that would be, and yet a higher percentage that would
cause you not to use the drug anymore because its now of little use to
human therapy. Dr. Tauxe, if you could answer that question, or maybe
one of your colleagues, I would really appreciate it. Because I think it
would help us define large T~x~.

FACILITATOR HESLIN: Sure. Is there someone on the panel who can respond
to that? If you would please, just \--

DR. TAUXE: I appreciate the question. I think it comes down to what, if
I could repeat it to make sure I got it, what is the clinical sort of
rules of primary thumb decide where concerns are. I think there are not
hard and fast rules that clinicians live by, but in general the range of
concerns might be characterized as noteworthy when theres a case report
of resistance. Gee, that means resistance is possible, and maybe thats
around 0.1 percent. Gee, I might actually see that in my practice in my
hospital, which is right around 1.0 percent. If resistance comes much
above five percent to a mainline treatment then theres serious
consideration that a change of one in 20 of failure is more than a lot
of clinicians would be willing to willingly accept.

I think clinicians as a group have a range of behaviors and a range of
different personal decision points, and they tend to think in terms of
percentages because thats the way they always get to express the
percent resistance of this or that in their hospital or their
experience. I think one of the innovations that I particularly like
about what were seeing in the threshold is to talk about a level of
defined harm that may translate into a percent resistance, but not just
simply saying the percent resistance itself is the only driver of it. So
Im saying sort of the range of concern is between probably 0.1 percent
as being the limits of detection in most sort of surveillance systems up
to five or 10 percent.

DR. DOWERSON: Okay. Thank you. Now I do have a continuation on public
comment then, and this is an alternative method. Im a toxicologist. So
were used to giving chemicals and seeing what happens. So another
possibility for developing a small t~x~ might be to take an animal
system, an experimental animal system like a rat or a mouse, colonize it
with bacteria that are indigenous to human or veterinary animal
practice. Im not sure how practical this is. This is just a wild idea,
and I did talk to some people that also echoed some of these ideas. So
maybe its not all that wild. Then on the basis of that new colony, this
mouse thats been colonized with a human flora, you give doses of
antimicrobial and you start to do a dose response curve in the classic
sense of how much resistance you get, and you use the results of that
model, if its even feasible, to estimate the small t~x~. Thats one
possibility. Thank you.

FACILITATOR HESLIN: Thank you. Yes?

DR. SHRYOCK: Thanks. Tom Shryock, Elanco Animal Health. Id like to
follow up on the concept that Dennis Wages had raised with regard to
prevalence. If you think about it, the use of prevalence in a national
monitoring program can be highly misleading and lead to some actions
that may not be warranted. It doesnt look like Dan Sahm is here, but if
he were he could elaborate upon this point using his TSM database. Dan
had earlier shared through some other meetings that they had an e. coli
fluoroquinolone-resistant monitoring level, and it was starting to blip
up a bit. So they wondered why. They started to look geographically
where that was happening, and they quartered that out into the United
States and found that the Southeast was where that rise seemed to be
occurring. They started to narrow down further and looked and found that
most of that rise was in Florida. Within Florida they found it was one
particular sector. In fact, it was one particular hospital, and in fact
it was one particular geriatric ward within that hospital. Apparently
they were using tremendous amounts of ciprofloxicin on geriatric
patients to treat urinary tract infections, and that one particular ward
was affecting the national prevalence. Interesting.

What was done? Well, they went back to that particular ward and began to
intervene appropriately. So that would be one apprehension that I would
share using national monitoring data where one outlier group skews the
entire national prevalence. Kind of that one rotten apple in the barrel
spoils the entire one. So thats a real caution that I have to think \--
that I think needs to be fully addressed.

Similarly, as you look at monitoring data, take DT104 for example, this
is an epidemic clone that has spread. Every time its found its counted
as a new isolate. Thats not right. Its one clone thats been out
there. If you keep counting it so you get 4,000 DT104s per year from
whatever species, thats kind of counting the same bug 4,000 times. It
puts a lot more weight on that particular clone. Dale Hancock has stated
that bio security is the way to prevent transmission. This threshold
wont account for that sort of thing. So that has to be factored into
this mix somehow.

Finally, its kind of amazing to me that theres a lot of talk about new
animal drugs in the pipeline. Nothing could be further from the truth.
In fact, there wont be any new chemical entities going into animal
health. Why dont we use examples of existing classes? Take tetracycline
and plug it into these equations and see what happens for example. I
think as you do that youll find that theres preexisting baseline
resistance out there already. How will that be accounted for in these
models when its in coli, its in a variety of salmonella, where its
part of that ACSSuT? How is that all going to be factored in here? So I
think the complexities that have to be dealt with have not been given
their due, and to oversimplify this model overlooks some of the reality
expectations that I would like to see included in there.

I guess I will conclude that we have a quality assurance department, and
one of the things that they continually say is, Do you want it right,
or do you want it right now? Thank you.

FACILITATOR HESLIN: Thank you. Yes, sir?

DR. BYWATER: Robin Bywater, consultant to Pfizer Animal Health. This
model has been put together very clearly with the fluoroquinolones in
mind linked with zoonotic campylobacter and salmonella. Even within that
context there are problems obviously as weve heard, and in particular,
and I think its already been referred to but I think its important to
remember, that the problem with the zoonotic diseases is not the
resistance. Its the disease itself, and even if we were able to solve,
in inverted commas, the problem of the resistance, the resulting benefit
in morbidity and mortality in the human population would be very
difficult if not impossible to measure it will be so small. So if we
take it from that and then look at other antibiotics the situation
becomes, as Tom has just referred to, incredibly complicated.

One of the more dangerous phrases we have in the document is that
resistance either attributed to in whole or in part. Its the words
in part which worry me. If you take practically any other group of
antibiotics, they are widely used in humans and also used in animals.
Theres a real danger that the very small part of the human problem that
might result from a use in animals will be take as sufficient for
draconian action. So well have action on the antibiotics in animals,
again having a minimal negligible effect on any benefit in human health.

I think were in the real danger here, and this is the question that I
wanted to put to the panel this morning, I think were in the real
danger of using animals as a scapegoat. Scapegoats were animals, too,
and with the same result. It didnt help the human population very much.
Thank you.

FACILITATOR HESLIN: Thank you.

DR. WILSON: Jim Wilson from Resources for the Future. This comment is
from the point of view of a risk analyst and not somebody who knows
anything \-- knows hardly anything at all about either the particular
business of the center of about veterinary medicine, and Im commenting
on \-- Im responding to the first question under Section 4, the
strengths and limitations. Clearly the strength of this proposal is that
you start it.

The limitations are really that the apparent simplicity of this direct
proportionality between two obscure measures of something hide an
enormous amount of complexity. By hiding that complexity the
consequences will be that the public wont accept it. The public will
not stand for not knowing how it is that you make connection from one to
the other. They want to see it laid out. They want to understand it.
They want people to be able to explain what are the cause and effect
relationships.

Ive come to believe after listening to the discussion and comments in
the last couple of days that the Agency has probably made it more
complicated for themselves than they need to have done. There are
probably simpler policy tools that they can use to achieve this same
end, and I will comment on that after the next session I think.

FACILITATOR HESLIN: Thank you. Yes, sir?

DR. CONDON: Robert Condon, RJC Associates. I guess the most troubling
thing I have with the assessment of the procedure thats given this
morning was the use of frequency of exposure as the dose. When you are
doing a risk assessment you want to get a good handle on exposure, and I
think its important that thats looked at rather than just how often or
how frequent. Because I dont think exposure to say 10 colony-forming
units per gram is going to result in the same outcome as exposure to a
million colony-forming units per gram. That, nowhere is that considered
as I can see in this assessment, and I think thats extremely important.
Because you can have a drug that results in a lot of very trivial
incidents and still have resistance, and thats going to be regulated to
a greater extent than a drug that has maybe very few incidences and
extremely high levels. I dont think the risk to the public is
equivalent under those conditions. I think somehow thats got to be
taken into account that just the use of frequency of exposure as a
surrogate for your exposure levels is not adequate.

FACILITATOR HESLIN: Okay. Thank you. Yes?

DR. CLAYCAMP: My name is Greg Claycamp. Im from the University of
Pittsburgh Graduate School of Public Health, and I also am a casual
observer here, not having much experience in a microbial setting. The
issue of complexity is pretty intriguing, though. In the session Dr.
Sahm mentioned and some of the audience have mentioned that each
antimicrobial class of chemicals, each organism, et cetera, might need
its own risk assessment and consideration because theyre so very
different.

While I dont doubt this complexity, I do have a more optimistic take on
the process. This is probably because I have the luxury, like
Dr. Thompson, of coming from fields in which risk assessment has been
around a little bit longer. For example, ionizing radiation and chemical
risk assessment, and it may be \-- well, I will make a couple of
observations here. One, the complexity of the human health issue here is
by no means unique in risk assessment, and, two, to focus all effort on
the complexity will lead to paralysis by analysis.

Just to back this up with an example, it may surprise some in the
audience, but ionizing radiation is far from being a monolithic hazard.
In fact, there are extreme differences not only in the physical
characteristics of the radiation, but in the pathways of exposure, the
target organs, and the interactions with other agents, including
microbes for that matter, and susceptible human sub-populations.

So how do risk managers deal with this complexity in other settings?
Well, for one, they stick to the core principles in the protection of
human health. They use modifying variables to account for the particular
characteristics of specific hazards within that class.

Now, does this remove the argument from risk assessment, risk management
models? Well, certainly not. I can tell from experience Ive witnessed
some 20-something years of arguments about whether the linear threshold
projection model is the appropriate model in ionizing radiation cancer
risk assessments. That wont go away, and in fact there are no direct
incontrovertible evidence of an increased risk at low-dose exposures.
Nevertheless, the regulatory model suffices for conservatism.

I think the CVM is to be complimented in this first pass at that
process. It is something that can be fleshed out with a lot of details,
but you do have to start with something, and we know that in fact the
epidemiology will never be sufficiently sensitive to detect the ultimate
results. We can only detect the accidents. So I submit that the
rationale that has worked here and that has been presented over the past
couple of days is a very good first pass and it should be continued.
Thank you.

FACILITATOR HESLIN: Okay. Thank you. Yes?

DR. COX: Tony Cox from Cox Associates. I didnt want so much to make a
comment as to ask some questions about the intent of the model, and by
the model I mean the H=KQ model. As I think Dr. Bartholomew said, a
certain amount of impact on health is proportional to a certain amount
of product consumed. Im trying to understand what this model is
supposed to be used for, what the intent of the model is for, and what
Im wondering \-- and this may be for

Dr. Bartholomew or for any of the other panelists who have a clear
understanding of what youre driving at here. What Im wondering is
arent we interested in predicting change rather than in measuring
levels? And if so, it seems to me that a lot of emphasis in the
threshold document is put on the fact that H, the health impact, is a
measured quantity and that Q, the cause I think of H, or the \-- whats
intended to be the cause, is also a measured quantity. It seems to me
that no matter how hard you measure stuff, it will never give you a way
of predicting how something will change when you change something else.

So question number one is, is this intended to be a predictive model? If
it is, what is it you are trying to predict? Change in what? Are we
looking at change in prevalence of cases; and, if so, what is that
related to? Change in what going into the model? Now, this model seems
to me to have all the earmarks of simplicity. Its only got three things
in it, H, K, and Q, but I cant or I havent yet understood what any of
those three things is, nor whether theyre inputs or outputs. So my
second question is, is K an input to the model or is it an output from
the model?

So question number one again is, what is it that were trying to \-- are
we trying to predict? If so, what are we trying to predict? Is it change
in something? And, secondly, what are we trying to predict it from? Is
there a change in something else? Again, I intend this not as a
rhetorical comment, but as the start of a conversation. Which is, what
are we trying to do with this model? Then philosophically Im wondering
how on earth measuring things is going to help us to predict whats
important to control.

FACILITATOR HESLIN: Is there a panel member that wants to respond or is
able to respond?

DR. SUNDLOF: I got volunteered. The model is not intended to be
predictive. The model is intended to set a level which we are relating
to public health, which is what we call an unacceptable level of public
\-- an unacceptable level of risk to public health, and thats the T(x).
We set that a priori, and we \-- and the model, if you will, allows us
to watch the resistance as it approaches that cap that we said we dont
want to have any more resistance beyond that. The assumption is that by
removing the selective pressure of the drug in the animal system that
threshold, once its reached, will not be exceeded if we can remove the
selective pressure of having that drug out there. It does not predict
the rate of resistance development. It does not purport to predict the
rate of resistance development. Thats something thats unknown, and I
would say probably unpredictable. It just says that if it does reach
this level that we would take the regulatory actions to remove the drug
from that animal species.

DR. COX: Thank you. Why do you need a model?

DR. SUNDLOF: Well, I think Dr. Lipsitch said it very well, and that is
that what you really need to know is what is the contribution of that
particular animal species to the overall harm to public health. What is
that particular species contributing to the overall resistance thats
observed in humans?

DR. COX: So the model is not intended to be used for prediction, which
relieves my mind considerably, because I didnt think it could be used
that way. Are you saying that it is to be used for attribution? Which is
that you look cases, and using the model you would attribute some
fraction of cases to antibiotics in chickens or in animals?

DR. SUNDLOF: Yes.

DR. COX: Okay. So then does the validity \-- or skip validity. Does the
usefulness of the model depend on the attributable risk formulas that
you use as part of your calculation?

DR. SUNDLOF: It depends on the ability to attribute that percent, that
resistance in humans back to that animal species. Yes. It does count on
that.

DR. COX: Then I think that Mike Ginovins question from before lunch
acquires greater relevance than I even thought it had, and his question
was when you measure attributable risk, when you calculate attributable
risk for something that is correlated with a number of risk factors,
each one of those factors ends up getting a great, big, attributable
risk assigned to it. So big that when you add them up you typically get
to more than 100 percent. Now, I see you are shaking your head. I trust
youre not questioning the arithmetic of that statement, because its
beyond doubt that correlated factors do in fact sum to more than 100
percent of attributable risk.

DR. SUNDLOF: In this case the only thing that were measuring is were
measuring the amount thats occurring in the human population and
partitioning that back to the animal. Is there a risk of
over-attributing it back to a particular animal species? Well, it cant
equal more than 100 percent.

DR. COX: Sorry. It can or it cannot?

DR. SUNDLOF: It cannot.

DR. COX: But it routinely does. A good example \--

DR. SUNDLOF: No.

DR. COX: \-- would be for campylobacter. As I understand it, an
attributable risk calculation based on a study that only looked at two
factors, namely chickens and cats \-- this was one of the two studies
relied on by CVM \-- found that 30 percent of attributable risk was due
to cats and 70 percent, this is for campylobacterosis of course, was due
to chickens. I think the author of the study is in this room.

Now, CVM used those numbers to justify an attributable risk factor of
close to 70 percent. But other studies not based on a student population
would also take into account things like drinking water, non-poultry
meat consumption, children in day care, foreign travel, which weve
heard a lot about. Several of those factors, such as non-poultry meat
consumption, affect at least as many people with at least as large a
relative risk as chicken. So you can see that if 70 percent is due to
chicken and at least as large a factor is due to non-poultry meat
consumption, youre already over 100 percent.

DR. SUNDLOF: The question is then, and then how accurately are you able
to capture the risk that is attributable to that particular species? And
I would agree that the better \-- the closer we can define that, the
better we can nail that down, the better our model will be. But the
alternative to that is what? Its to look at other individual pieces
that contribute to that. How the product was handled, how it was
transported.

DR. COX: But I think the alternative, and even the routine alternative
in areas such as path analysis of biostatistics, is to distinguish
between the association of a single factor and outcome and its direct
causal impact after correcting for the effects of all confounders. Now,
before the break, I thought I heard Dr. Bartholomew explain that
adjusting for confounding was unnecessary in this context. But it seems
to me that if youre in fact performing attributable risk calculations,
if that is the primary purpose of this model, that its not an
predictive model, its an attributive model, then surely you need to
adjust for confounders and for other risk factors in correctly
calculating attributable risk. Again, Im aware of a large literature on
how you should calculate attributable risks in the presence of joint
causes, and I know that the attributable risk calculation performed for
campylobacter didnt use the multiple-factor formula. It used a
single-factor formula.

DR. SUNDLOF: Okay. Mary said shed talk about that.

MS. BARTHOLOMEW: Okay. Yes. We recognize that there are problems in the
campylobacter risk assessment, in particular with attributable risk.

VOICE: Louder, Mary.

MS. BARTHOLOMEW: Im sorry. I have to hold this.

DR. COX: Say it loud and say it proud.

MS. BARTHOLOMEW: Okay. We recognize that as one of the limitations in
doing a risk assessment for attributing disease to a species, and one of
the things that we mentioned in the draft threshold document is that
since that is a controversial parameter, and its hard to measure, it
depends as you say on the number of the factors and it particularly
depends on what specifically was asked. If you ask the question a
certain way, youre going to attribute it to chicken when in fact it may
have been the lettuce that was stored next to the chicken from which it
actually came. Or you may think that its not chicken because you didnt
\-- excuse me. Lets see. It came from the lettuce, and you ask the
question about chicken, but you dont ask about lettuce. The person
didnt eat chicken, so therefore its not going to get attributed to the
chicken from which the cross-contamination came.

So one of the things that we suggested in the threshold document is that
we might need to establish attribution by means of scientific panels or
somehow getting consensus about what would be fair values to use in
attribution. I think it was Dr. Sahm who mentioned that the Danish had
found a way to attribute salmonella to their different species, and
perhaps we can learn something from them about how that would be. But in
general we recognize that as a problem area and think that there \-- if
you want to regulate you can do it by consensus sometimes when you cant
measure.

DR. COX: Thanks. I think thats a very clear statement. I think Ill
step down, summarize and step down. My understanding of what was said is
that the intent of the CVM threshold model is not to make predictions,
but is to attribute risk, that we recognize that the attribution of risk
is wrought with problems that are currently not all solved, that other
groups have worked on the problem, and we may get some wisdom from those
other groups.

I add as my final comment until next time that I think that attribution
of risk in the presence of joint causes, which is what were dealing
with here, is laden with policy judgements; and its important to label
them as policy judgements and not as objective science or objective
epidemiology.

MS. BARTHOLOMEW: Yes. We will attempt to make the distinction.

DR. COX: Thank you.

FACILITATOR HESLIN: Okay. Thank all of you. Yes?

DR. BELL: I am David Bell from CDC, and I have found this discussion
fascinating. But I think its time I find myself wanting to step up here
and commend the FDA for the tremendous progress they have made in
tackling this issue in the last two years since the framework document
was proposed and was recommended for approval by the Veterinary Medicine
Advisory Committee. I find now that were into this discussion of all
the complexities of antimicrobial resistance and will \-- you know, in
the variations with different drug-bug combinations which we all know
are present and to what extent will resistance go down if a drug is
removed and the attributable percentages, et cetera.

While this is a very important discussion to have, I think we cant lose
sight of the fact that the question is given that were going to need
new antibiotics in the future for human medicine, and I presume for
veterinary medicine as well, the question is when a new drug is
submitted to the FDA for approval for use in veterinary medicine what is
the FDA supposed to do? And how is CDC supposed to regard application
from an animal pharmaceutical company for a drug for animal use when
this drug is important in human medicine? This is what the FDA has been
trying to grapple with, and we had this discussion two years ago at the
VMAC Committee about how, you know, after 30 years of disputes and being
sunk in complexities and that no \-- theres never going to be any
consensus between the different communities. What realistically is the
FDA supposed to do so that this becomes an orderly process and doesnt
fall out onto the pages of the [New York Times]{.underline} and the
halls of Congress and so on, and this pioneering notion that, well, you
would monitor in the animal products for maybe at slaughter or on the
farm or at retail. I dont know. You would monitor human resistance. You
would look at to see if there were increases correlated, and that would
enable us in public health to have some confidence that maybe it was
okay to let this drug out for approval because there was a monitoring
system, and if the results in \-- if the human resistance rates were
rising and this was supported by a correlation with the rising
resistance rates in animals then there would be an expeditious process
to take mitigating action and get the drug, if necessary, recalled.

Then we wouldnt have these endless, insoluble debates about whether
this is going to happen or not, because we dont know if its going to
happen or how soon it will. Im very sympathetic to Dr. Sahms very
eloquent presentation that resistance hasnt developed in 60 years to
certain drug-bug combinations but has with others. You know, I mean,
this is the question.

So I dont know that we need to analyze all the complexities of how
antibiotic resistance is acquired in different elements of society and
what all the contributory and causatory factors are. The concept was, at
least as I understood it, that if resistance starts to rise in humans
and in animals, and its matched and its pretty clear, we dont have to
have a long process to get it back. I think that we need to help the FDA
come up with this. Yes, this initial effort needs to be refined, but I
guess I hope that we dont throw the baby out with the bath water.
Because this is what we all need to end up with so that we have a
process to get the farmers the drugs they need and also make sure that
the human medicine isnt compromised as result. I just find myself
wanting to keep in sight the fact that the FDA is really doing a good
job, and I hope that all this isnt really opposition. Its constructive
comment, and we have to help them do something thats fairly simple.

FACILITATOR HESLIN: Okay. Thank you. Youve been standing there a while.
So why dont you just go next?

DR. ANGULO: Im Fred Angulo. Im also from the Centers for Disease
Control, and to add to Dr. Bells comments, I wanted to \-- Dr. Bell
mentioned that at the VMAC meeting we wholeheartedly endorsed the
consensus that there needed to be a new paradigm, and we also agreed
with the constructive comments that people made that there needed to be
a risk assessment and endorsed the idea of a risk assessment.

In fact, there was a risk assessment done, and I just wanted to add to
Dr. Bells comments to point out how strongly we endorsed the risk
assessment that was done. Because the risk assessment on the
campylobacter and fluoroquinolone use risk assessment appears to \-- is
the foundation upon which this threshold is built, and it is \-- and yet
there has been criticisms placed perhaps up on the risk assessment here
at the meeting.

But we do believe that the risk assessment had an appropriate
attribution of the proportion of campylobacter cases due to the poultry
reservoir which was an attribution of 70 percent. It was not an
etiological fraction or attributable risk, but an attribution for that
commodity group. Thats an appropriate attribution to that commodity
group for the source of campylobacter. In fact, that was reiterated at a
recent WHO \-- World Health Organization consultation in Copenhagen on
campylobacter where other public health experts from around the world
also attributed poultry as the \-- somewhere around that order of the
source of campylobacter.

So that was a reasonable assumption that they made, and then they used
data. There was someone who said we should dry-lab this or we should \--
what would this model do, what would K-res be if you were to model. They
used real human outcomes, and they developed K-res based upon 1998 data
of real human outcomes. They put it up for public comments. They
received innumerable comments. We had a public meeting, and they took
those comments into consideration.

Then they published, and reconsidered it and published it again with 99
data. So two years worth of data. They dry-labbed it again with people
data, data of people outcome again, and they showed that the
relationship was fairly similar. K-res was around the same location,
that the relationship was appropriate.

So hence our strong support for the risk assessment and the strong
support for the idea that a K-res can be developed, and our strong basic
scientific support that it is in fact true that when there is resistance
in the animal reservoir there can be resistance in humans from foodborne
illness and that in fact, little h can equal human health outcomes.

FACILITATOR HESLIN: Thank you. Yes?

DR. COX: Fred, would you stick around?

FACILITATOR HESLIN: Id like to say that Im not sure that its intended
to be a dialogue here between two individuals.

DR. COX: I was hoping more of a conversation with the group.

FACILITATOR HESLIN: Okay.

DR. COX: Its hard to do with 250 people, I agree, but that seems to be
the forum. With your permission, because I think that Freds points are
substantive, Id like to understand two things. One is, what is the
basis for thinking that 70 percent is a reasonable attributable risk for
chicken? Do you agree that CDCs own data, the Freedman et al 2000
study, showed that the risk attributable to non-poultry food is at least
as large as the risk attributable to chicken? And if so, how can you
reconcile that with 70 percent being reasonable?

Then, secondly, for K-res being stable over time, I have to put in my
own personal comment since campylobacter is now something of a hobby of
mine, that the data remain fixed at about one-third the value that was
calculated using an incorrect \-\-- in methodology. So if we want to
talk about campylobacter, theres a lot of detail. But the fundamental
scientific support that you allude to I believe is completely lacking.

DR. ANGULO: Okay. One comment is that there was a remarkable difference
that occurred between the 99 human data and the 98 human data.
Fortunately in this country we had the implementation of HACCP, and we
have seen a decline in human illness which we have document with FoodNet
with campylobacter. The difference between the 98 data and the 99 data
in terms of human burden of campylobacter was a decline which we \--
which might be attributable to the HACCP implementation. But even with
that, even taking into account the decline in human illness, you are
still able to calculate and demonstrate the association between the
little h and the human health outcome.

I think the fundamental \-- and it was in the tone of your conversation
with Dr. Sundlof, is when you were speaking about attributable
proportion and etiological fraction, or whatever term you would care to
use \--

DR. COX: Well, attributable risk. Yes.

DR. ANGULO: Attributable risk. I agree that when you do sporadic case
\-- we do case control studies and sporadic case control studies. You
can get attributable proportions, and we know that theres a lot of
noise generated when you derive that value. Thats different than the
idea that you can look at the scientific literature and attribute a
proportion of illness to a commodity. Not based on a sporadic case
control study where all the noise inherent upon getting the point
estimate is evident, but we can attribute a proportion of illness to a
commodity reservoir.

DR. COX: On what basis?

DR. ANGULO: ON the basis of \-- well, we look forward to those
discussions on how we would do that.

DR. COX: No, no. What I mean \--

DR. ANGULO: And I think that expert consultation is one way that thats
done. I think that our experience through foodborne disease outbreak
investigation, through our experiences on the effect of interventions
and their consequential decline in human illness, that there is \-- we
can rely on the expert public health community to help us derive
attribution of commodities. There will be imprecision there, and I guess
thats my last point, is, yes, there will be imprecision. Is it 70
percent or 69 percent or 65 percent or even 40 percent \--

DR. COX: Or 10 percent as I believe.

DR. ANGULO: Or even 10 percent.

DR. COX: Yes.

DR. ANGULO: If it was 10 percent, then instead of 8,000 illnesses \-- I
dont know what the mean was in the last 99 data, but instead of 8,000
illnesses it would 800 illnesses. There still is consequential illness.
Even if you try to modify the attribution, theres still consequential
illness.

DR. COX: What do you mean by consequential there? Do you mean cause and
effect?

DR. ANGULO: Well, we are getting detracted, but the key thing I think
Im trying to say is I do believe that its clear that there is
attribution of human illness to food animal reservoirs that when you get
public health officials together they can come up with a fairly precise
attribution to that commodity.

DR. COX: Okay. I would like to know how that is done. Because the
numbers that I see are inconsistent with the methodology thats used.
This is nothing to do with noise. This is nothing to do with sampling
variation. The methodology that is used is such that if I have 10
factors, A, B, C, all the way through J, and if each factor is either
present for an individual or absent for an individual, then every
individual \-- Im sorry, every factor will have the same attributable
risk assigned to it. That means that if factor one has a 70 percent
attributable risk, so will factors two through 10, or B through J as I
said a moment ago. Which means that 700 percent of total risk will be
attributed to those 10 factors. That is not a sampling problem. That is
a conceptual problem.

Ive stressed before, and I will just say one more time, attribution is
not a matter of measurement. It is not a matter of a science. It is a
matter of policy judgement, and if we take policy judgement and
masquerade it as fact, weve moved away from good risk analysis.

FACILITATOR HESLIN: Okay. Thank you. In the interest of trying to stay
on schedule, I see theres at least three more people who want to
comment. So there should be at least enough time for the three of you.
Go ahead.

DR. KEELING: Thank you. John Keeling with the Animal Health Institute.
Dr. Bell, I wanted to kind of address your comments. I would agree with
you that there is probably \-- there is no group in this audience who is
more frustrated than you about the fact that the time that has gone on
while we have pursued this difficult proposition. But I would suggest to
you that, you know, it does matter, the issues that are being brought
up, because it does matter in us ending up with a solution to this that
addresses your concern. That is that we be able to look in a thoughtful
and flexible way at this drug use and make prudent decisions. For
instance, since this model doesnt contain any way to adjust K for
changes in handling practices, we really dont have any way over time to
adjust the model to take those kind of things into consideration that
you would want to do in terms of maintaining both the efficacy and the
availability of those products, both for humans and for animal.

Im reminded of the story my father told me about the drunk standing
under the street light. When a man approached him and said, Well, you
look agitated. Whats the problem? He said, Well, Ive lost my keys.
And the man said, Well, where did you lose them? He said, Well, I
lost them way over there. And the guy says, Well, why are you looking
for them here? He says, Because this is where the light is.

So I hope we wont restrict ourselves to just standing under the light,
that well look to see where the keys really are, because I know thats
what you want. But there was an element of do something even if its
wrong in your frustration, and I \-- sometimes we feel the same way.

FACILITATOR HESLIN: Thank you. Yes?

DR. BLOOD: My name is Shawn Blood, and Im the Chairman of the Committee
for Pharmaceutical and Biological Issues for the American Association of
Bovine Practitioners. The bovine practitioners are a 5,000-plus member
group of practitioners servicing the beef as well as the dairy industry.

As Dr. Wilson indicated yesterday, our veterinary oath committed us to
human health as well as animal health, and we encourage judicious use
guidelines as well as pathogen reduction efforts at all levels of
production. However, we are concerned about our ability to address
therapeutic needs of patients pain and suffering.

As in human medicine, we are concerned about changes in susceptibility
that may effect efficacy in veterinary and human medicine. However, we
are concerned about the potential to have responsibility of this
antimicrobial resistance assigned to us in a less than equitable manner.
We appreciate the opportunity to continue to provide comment and to
continue to have involvement in this issue at this level, and we would
also encourage consideration to using a model to specific production
systems within an industry and not necessarily to the specific commodity
as a whole. Different production systems can have different confounders
and risks. We would also encourage you to carefully consider the
practice of using the same model across and even within commodity
groups, because they are all different. Thank you for the opportunity.

FACILITATOR HESLIN: Okay. Thank you. I know you were moving towards the
microphone. I think after this gentleman speaks there will be a few
minutes yet.

DR. CONDON: Robert Condon again. I want to thank Dr. Sundlof for his
clarification, because I think thats an area that now allows for some
progress to be made if we get away from trying to do a quantitative risk
assessment, do an exact prediction, but to get into trying to get a
mechanism to assess the outcome and the safety. I look at this similar
to applying a 100-fold safety factor to a chronic study. We dont assess
the risk there. Weve got a procedure that works. It doesnt say the
risk in humans. It just says if you take 100th of the effect level or
the no-effect level from a chronic study and apply that to humans thats
acceptable.

I think maybe thats an approach that CVM may want to look at and try to
get away from, because doing a quantitative risk assessment is just \--
is easily seen as fraught with a lot of assumptions. I think youve got
to be aware when you make these assumptions they need to be explicitly
stated, just as the attributable risk. How much is coming from chickens,
from different animal species, how much is resistance. The estimates you
have are biased. Theyre an upper. They probably attribute more than is
actually there, because youve studied a limited number of factors.

Theres nothing wrong with that. You just got to recognize thats the
situation. Maybe make a statement to the degree of bias. If you only
look for two factors then theres probably quite a bit. If you look at
100 factors, theres probably less. But its a function of whats looked
at. Theres a bias in that estimate. In this case, it attributes too
much, so its a conservative bias. I think those type of things need to
be built into the process in the statement and get away from trying to
say that the true effect level is XY or something. Just say, okay, were
going to use a procedure thats going to get us a level, and were going
to take that level as being acceptable and not try to predict the exact
level.

The other place thats biased is in your human estimate. Those
incidences are not all independent. If you have 20 people go to a party,
eat a contaminated salad, all 20 get sick, youve got an incidence of
20. Is that 20 independent incidents, or is that one? When you start
lumping those all together considering them independent then youve got
a bias. You are attributing too much, more cases than actually occurred.

So those type of things I think at each step when those factors are in
there an assessment needs to be made of what the bias is and what \-- if
you can make a potential of the magnitude or how much it is, that gets
you a more honest estimate and lets everybody know exactly whats going
on. But these are in all the estimates. Theres a degree of bias and our
lack of independence which affects the outcome. I think some of those
factors need to be taken into account and, as Dr. Sundlof said, not make
a statement that this is a predictive, but its a tool for regulation.

FACILITATOR HESLIN: Thank you.

DR. BELL: I just want to add that my only plea was that we keep the
broader perspective and we not throw the baby out with the bath water. I
mean, we have all been frustrated by the lack of availability of
important new drugs in human medicine. Im told my colleagues are
frustrated by the lack of availability of drugs in animal medicine. The
way were going to deal with the antimicrobial resistance problem in the
future is only partially through appropriate use of drugs and vaccines.

What we really need are new drugs. We need a pipeline of new drugs, and
then the question is as I said, when one comes to the FDA for approval
in animals what are they supposed to do? I think that the complexities
of the problem, its been illuminating to point them out here, and Im
not a modeling expert and I \-- you know, Im sure the model can be
tuned and needs to be tuned, but I would \-- I think the question is we
need to help the FDA answer the question. What are they supposed to do
when they get a new animal drug, and at the moment were too nervous to
be, you know, comfortable about saying that important drugs in human
medicine should just be approved for use in animals because we dont
have any mechanism to get them back if it turns out that resistance
becomes a problem. Resistance is unpredictable. We dont know whether
its going to become a problem. So we have to help the FDA do something
like this, and thats the only perspective I just wanted us to keep in
mind.

FACILITATOR HESLIN: Okay. Thank you. Yes?

DR. RHODES: My name is Linda Rhodes. Im a veterinarian, and I work for
Merial Animal Health Company. Im not a microbiologist and Im certainly
not a risk assessment person, but it strikes me that this conversation
about the risk assessment is very much like the Florida recount. If you
are a Republican, you can make some very good arguments of why we
shouldnt count the vote, and if you are a Democrat, you can make some
very cogent arguments of why we should.

It strikes me that the professional risk assessors who have spoken to us
and made comments during this period have pretty much a one voice, which
is that this is a good beginning but also a deeply flawed model. It
strikes me that the M.D.s who speak to us also speak pretty much of one
voice, and that voice is we must do something because we have a crisis
on our hands. So it strikes me that in fact were dealing with somewhat
of a disguised political conversation here, and I think its very
important to depoliticize the conversation, and I commend the CVM for
doing that by having an open forum like this and inviting everyone to
the table.

My big concern here is that it seems that there is a prejudice towards
saying simple is better and action is better than inaction. I would
challenge both of those thoughts. Most physicians have an oath that says
something like at first do no harm, and I would submit that there is a
possibility that a simple action may in fact be a harmful action. So
doing something may in fact cause more harm than doing nothing. We have
to be very careful of the push towards doing something just because
something is there to be done.

The only thing I would like to say finally is in the plea for new drugs
that we hear from our medical colleagues. I think its important to
remember what most of us learned in epidemiology 101, which is that the
biggest impact on public health over the past 100 years has not been
penicillin. It has been toilets and hand washing, and having physicians
wash their hands between patients when theyre delivering babies. These
things have had a much bigger impact on public health than antibiotics,
and I think we should keep that in mind in the context of this
discussion.

FACILITATOR HESLIN: Okay. Thank you. Any other comments? Okay. It looks
like were back on schedule, and the schedule says were due a break
right now. If you take 15 minutes and return promptly, 15 minutes.

(Whereupon, a brief break was taken at 2:20 p.m.)

**[SESSION 5: ALTERNATIVE APPROACHES TO THE CVM PROPOSAL]{.underline}**

MODERATOR MITCHELL: I would like to begin Session 5, if you would take
your seats please. I am going to begin Session 5. This is a session on
alternative approaches to the CVM proposal, and we have three speakers
for this session, which will be followed by a public comment period
facilitated by Jim Heslin again.

***[Alternatives to Establishing Thresholds]{.underline}***

***By Dr. Richard Carnevale***

MODERATOR MITCHELL: Our first speaker in this session will be Dr.
Richard Carnevale, who is Vice President, Scientific Regulatory and
International Affairs of the Animal Health Institute. Dr. Carnevale
obtained his doctorate in veterinary medicine from the University of
Pennsylvania in 73 and a bachelor of science degree from the University
of Arizona in 69. He has also served as Assistant Deputy Administrator
in the Science and Technology Program for the USDA Food Safety and
Inspection Service. Prior to that he was Office Deputy Director the for
the Food and Drug Administration Center for Veterinary Medicine Office
of New Animal Drug Evaluation, and he has served in his present capacity
for the AHI since 1995. Dr. Carnevale.

DR. CARNEVALE: Thank you, Dr. Mitchell. I guess I have an advantage
going the third day of the program. I get to comment on everything Ive
heard for the last three days. Although Im not going to try to do that,
but with certain exceptions I may comment on some things that were said.

I want to thank CVM for inviting me to take part in this session. I have
been tasked as a representative of the animal health industry to try to
provide maybe some alternatives to the threshold document and the
approach that CVM is proposing here, and I greatly appreciate the effort
that CVM has undertaken in trying to apply a more objective approach to
the regulation of antibiotic resistance. But Im afraid that the
complexity of this issue is such that \-- and weve heard the last two
days the attempts to apply such a precise proportional relationship to
this issue is an extremely hard event to undertake.

It seems that every meeting I go to, and this meeting is certainly no
exception, there is always new information that we hear that just
confuses it even more in my mind, and probably every mind, everybody in
the audience. So I take sympathy with the Agency in trying to deal with
such a complex issue, but the industry is really in a difficult position
as well in trying to respond the public health concerns that are being
presented to it and trying to get through the regulatory morass that is
created by these kinds of issues.

Im not a risk assessor, clearly, and I have had a very difficult time,
as probably many of you have, in trying to understand the concepts and
mathematics expressed in the document that CVM presented. Actually its
been a lot better the last two days, because I think all of us have come
to appreciate a little bit more actually what the document is trying to
do. However, because the document and the concept is grounded in risk
assessment in the model that CVM developed for campylobacter in
chickens, I think its become evident that it suffers from some of the
same problems that you have identified in the risk assessment model and
AHI has previously identified in the model. I also obviously sense that
the Agency might be having some difficulty, too, understanding the full
ramifications of what its proposing.

In short, Im probably going to repeat a number of things that people
have said about the model, but I think we have several concerns that I
wanted to highlight. While CVM says that its a simple model and
simplicity is important for them, I think the process of trying to model
is not simple. Clearly, this is a complex, difficult-to-understand
situation, and a model that is too simple may not reflect reality, may
not reflect in reality what the true public health impact may be, and
clearly might be biased too far in one direction.

For example, as has been said, it treats all chicken the same and
doesnt take into account that not all chicken is delivered to the
consumer in the raw state. A large proportion of chicken, as has been
said today, is precooked, which virtually eliminates any risk of
pathogenic bacteria.

The model also treats any level of bacteria as equally capable of
causing disease. We know that infectious dose may differ for different
types of bacteria. Because levels of bacteria only are considered, the
prevalence of bacteria will be dependent on how that sample is taken.
For example, if you take grams of sample versus tons of sample you will
get a different result for prevalence. We would certainly prefer to see
a CFU per unit of sample looked at rather than simply prevalence. That
data is available through the FSIS database.

Its also basing a threshold determination on the database, and thats
the National Antimicrobial Resistance Monitoring Program, which, while
AHI strongly supports and would like to see strengthened, was not
designed with the kind of statistical rigor that I think is necessary to
use as a true national incidence. So to use the NARMS database, while
its useful, again we have to remember it was not statistically designed
with the kind of precision that I think is wanting to be applied to it
now by this threshold concept.

Finally, it assumes an absolute relationship between resistance in
animal in isolates and resistance in human isolates. Weve heard this
morning the relationship is not all that clear as to what that real
relationship is and how significant it is for certain foodborne
pathogens. Because of the shortcomings, it is our opinion that the tight
proportional relationship CVM has proposed to establish in arriving at a
resistance percentage threshold may not at all result in regulatory
limits that are truly indicative of public health risks. Were dealing
with biological systems with a great deal of uncertainty and a great
deal of assumptions that have to be made. The analysis does seem
appropriate maybe for a project in engineering, but Im not sure it is
useful for the kind of \-- to measure precisely the relationships were
dealing with here. Its fraught with the number of problematic factors
were dealing with.

So I guess the bottom line is while we support the continued development
of this model and enhancing it, and trying to take into consideration
some of the comments made today, AHI cannot support nor the industry can
support the use of a bright line standard coming out from such a
threshold concept and model as a basis for removing a product from the
market. Dr. Tollefson said that once a bright line standard was achieved
thats what would happen, a product would be removed from the market.
Im not sure how CVM would accomplish this without a change in the law,
unless they propose to consider the bright line standard as an eminent
public health hazard, which to date I havent heard that stated.

I think we have to be aware that while the industry would be asked to
agree to such a bright line standard, the FDA would be asking the
industry essentially to be giving up their due process in their right to
question why the product should be removed from the market. Currently,
it costs

\$20 million to \$50 million to get a new food animal drug on the
market, and probably seven or more years of development time. I think
its going to be difficult to ask a drug company to agree to an
arbitrary number that might come out of such a threshold model as the
only criterion for them to remove the product. Once you reach that
bright line, you take the drug off the market. Its certainly going to
stifle drug development. It already has stifled drug development, just
the threat of that.

Let me make a comment on that. I think people need to realize that there
isnt an overflowing cauldron of drugs in the developmental pipeline for
animals. There may be a misimpression that there are a lot of drugs
under development. Frankly, there arent. There may be a couple that
have been in the process for some time. Certainly with the uncertainty
of this antibiotic resistance issue there really havent been any new
ones to come into the approval process. Over the last 10 years, I can
count on one hand how many new antibacterials have come out for food
animals, and probably three fingers would do that.

So we need to understand that the drug approval process is already very
rigorous. It already is very difficult to get a product through to
market. This is certainly not going to enhance that ability to get drugs
on the market for therapeutic use in animals.

Now, thats not to say that, as I said before, that we would not support
the development of this model and developing some kind of a threshold
and some kind of a percentage resistance as some kind of a guideline for
future activities to control and mitigate resistance. I have to applaud
FDA for the heroic effort it really has attempted in doing this, in
trying to take on this enormous task, but I would harken back to a quote
that Dr. Cathy Wotecki, the Under Secretary for Food Safety made at a
recent Georgetown conference. The FSIS of course is charged with
regulating meat and poultry safety, and when they undertook the
development of HACCP standards, the current salmonella testing that is
going into the NARMS program, they looked at trying to establish a safe
level for salmonella and meat poultry. They frankly decided they could
not do that, because the data wasnt there to attribute a specific risk
of a specific count of pathogens on meat and poultry to human health. So
what CVM is undertaking is something that USDA frankly chose or decided
that they could not undertake. So clearly what CVM is trying to do is
very heroic.

It appears from the comments made at this meeting that the industry,
both the industry and the public interest groups that have spoken here
today are against thresholds, but clearly for different reasons. On the
one hand, some believe that no thresholds can be set for certain
antibiotics of importance to human medicine, and hence these drugs
shouldnt be approved at all. As far as the industry is concerned, we
dont believe that such hard numbers can be set which are other than
arbitrary in the final analysis and could jeopardize millions of dollars
worth of product development in a very short period of time.

So now that I have criticized FDA let me offer some suggestions, and I
dont come with any magical solutions, because I dont think there are
any magical solutions to this issue. I think we need to recognize that
antibiotic resistance does not fit the usual paradigms that FDA operates
under. I sense in listening today that FDA is trying to operate, is
trying to implement an antibiotic resistance policy that is very similar
to the way residues are regulated, and I think thats where the
difficulty lies. In attempting to do that, they are running into
enormous problems because residues arent the same as pathogens. There
are enormous differences between the way thats done, and I see a lot of
similarities to the use of the risk cup and the apportioning of risk as
is done with chemical residues.

So I think the intent of the agency to establish tolerances, if you
will, for antibiotic resistance simply wont work, and I think we just
need to recognize that. We need to look at another way of doing it. So
let me offer the following. I think first of all we should clearly
strengthen the post-marketing surveillance program, NARMS, to include
additional antimicrobials, pathogens such as campylobacter, and if
possible redesign the system to represent a national prevalence. Make it
statistically robust. If possible, it should also be structured so that
regional data could be gathered and analyzed to examine geographically
differences. I also heard Dr. Tollefson mention that FDA may be moving
to retail sampling. While retail sampling is useful to examine what may
be out in the marketplace downstream from the slaughter plant, we would
have significant problems supporting the use of retail sampling as a
basis for regulation. Simply because once the product gets out into the
marketplace the numerous factors that can affect contamination of that
meat and poultry from food handlers, inadequate storage and
refrigeration in the supermarket and other sources of contamination
thats going to bias the results or change the results of what actually
came out of that plant. So it would really be hard to use that data to
really kind of measure or regulate the industry based on whats found
there. Not to mention the fact of the logistic difficulties in trying to
get an adequate sampling of retail establishments considering the number
that they represent.

Along with strengthening NARMS, we think that we need to establish
official resistance breakpoints that correspond to likely clinical
outcomes in humans for all important foodborne pathogens. Its very
important that were operating under the same standardized laboratory
methods and breakpoints.

Now, the major part of my recommendation would be that because this is
such a difficult issue, the Agency is having a hard time grappling with
it internally, the industry is having a hard time with the Agency
grappling with it internally, we would suggest that an independent
expert third-party group be appointed. Composed of microbiologists,
veterinarians, epidemiologists, other public health experts, and other
scientists if necessary, statisticians possibly, to evaluate the NARMS
data on a periodic basis and advise FDA of trends in resistance that are
occurring that may be of concern. The group could also recommend to FDA
that follow-up investigations to determine the cause of the resistant
trends might be indicated.

AHI has a problem understanding why the immediate reaction of the Agency
to an increase in resistance levels in NARMS should trigger the
immediate banning of a product. Now, I dont want to harken back to the
residue paradigm again, but frankly with residues that doesnt happen.
If residues occur, the Agency doesnt go to ban the product. The Agency
investigates where those residue violations are coming from and then try
to get down to the producer level or the veterinarian level to find out
why that occurred.

It seems to me that kind of follow-up action could be taken in NARMS.
After all, the NARMS database is very small. There are not 1,000s of
samples in that database. When there are blips in the resistance
percentages it may very well have been due to a few samples coming from
a certain geographical area. Without investigating that, FDA risks the
action of taking a drug off the market in all 50 states when in fact the
problem was coming from a certain region. Redesigning the NARMS go get
regional information could help with that problem.

Given a properly developed risk assessment model, and we think the model
could be enhanced, the model that CVM has developed, the resistance data
from NARMS could be plugged into the model to determine what level of
risk the observed resistance is likely to present. Based on a
determination of whether the risk appears to be low, moderate, or high,
then the Agency would be in a better position to determine if an action
is needed along the lines of altering the product label, restricting
certain uses, educational efforts, or in the worse case temporary or
permanent product removal. Developing a comprehensive program in
conjunction with the ADMA, industry, and producer groups to embed
prudent use guidelines into the usual practice of veterinary medicine.
Educational programs in veterinary schools and colleges of animal
science should also be strongly considered.

Of course it goes without saying that any new antimicrobials that are
approved for therapeutic use would be either under the direct control of
a veterinarian or as a veterinary feed directive drug. We would also
support seeking additional restrictions on those drugs that have
critical importance to human medicine, such as possibly the prohibition
of any extralabel uses as is currently with fluoroquinolones.

Those are some brief thoughts on the topic. I recognize this wont
satisfy all of those who want absolute action on this issue, but I
really dont believe theres a quick fix. I dont believe theres a
bright line standard that can be adequately designed in a timely fashion
for the industry to be able to respond. Ive heard a lot of agreement,
however, that therapeutic antibiotics are necessary for the treatment of
animal disease. I havent heard too many people disagree with that
concept. So it seems to me that we should be able to evaluate the
continued microbial safety of these compounds without having to
establish unyielding standards that place both the Agency and the
industry in an inflexible position. Otherwise, Im concerned with the
Agency being faced with operating under a zero risk policy and
essentially a precautionary approach, which would mean that they could
not approve any drugs at all, and I think youd have a great impact on
animal health and eventually food safety. Thank you for your time.

(Applause.)

MODERATOR MITCHELL: Thank you, Dr. Carnevale. I should identify myself
for the purposes of the auditory record over at my right. Im Dr. Bert
Mitchell, Director of Policy and Regulation, the Center for Veterinary
Medicine.

***[Alternatives to Establishing Thresholds]{.underline}***

***By Dr. David Smith***

MODERATOR MITCHELL: Our second speaker will be Dr. David Smith, who is
an Assistant Professor at the University of Maryland in Baltimore,
Department of Epidemiology and Preventive Medicine. Dr. Smith obtained
his Ph.D. in 1998 from the Princeton University Ecology and
Environmental Biology program. He also has an M.A. from that same
program, and an M.S. from Brigham Young University as well as B.S. Of
interest here in particular today, he is a co-investigator in a Pfizer
Corporation grant, risk assessment in the use of quinpristin and
dalfopristin in animals and animal fed; preliminary study data
evaluation beginning in January of this year with Glenn Morris at the
University of Maryland. Dr. Smith.

DR. SMITH: First of all Id like to recognize that since being invited
to talk I asked Judy Johnson to be a coauthor with me on the paper. Its
always nice to have a real microbiologist when youre a population
biologist and not as close to the organisms. So I rely on her a lot for
getting the biology right.

The question I want to ask today is whether threshold models are
appropriate for all antibiotic resistance; and, to clarify where Im
coming from, Ive been thinking about the risk assessment for
streptogramin resistance and enterococci, and we dont believe \-- I
will give you the preview.

We dont believe that the framework for the campylobacter risk
assessment is completely appropriate for enterococci and some other
organisms. So Id like to discuss the issues that arose in thinking
about what happens in a hospital and directly thinking about the links
between what happens on a farm in the use of antibiotics and the
consequences in a hospital.

(Slide.)

So the outline of my talk today is quite simple. Im going to discuss
the general idea of what a threshold is and some different kinds of
thresholds, talk about the relationship between antibiotic use and
antibiotic resistance, and the context of having identified what
threshold were actually talking about. Talk about the difference
between these commensal and zoonotic organisms, something that Marc and
some others have talked about, ask the question of whether there is a
reasonable expectation of no harm, and then talk about our proposal for
a simple \-- fairly simple drug-centered policy.

(Slide.)

So when we talk about thresholds, there are lots of different kinds of
thresholds. My house has a threshold. Its something you cross, and
presumably a threshold is important because when you cross it something
else happens. So a lot of times we think about an exposure threshold as
something like an infective dose, where small doses are acceptable and
larger doses cause some kind of damage thats unacceptable.

Or you could have a trigger; and a trigger is something where nothing
happens at all until you cross the critical point, and then there is a
sudden, irreversible consequence. Like a trigger on a gun, the point of
no return on that trigger. Or you could have a set point on a
thermostat, where the heat is on until you \-- the temperature increases
above the set point, and then the heat is turned off and the temperature
presumably goes back down. You could have a detection threshold, which
have come up in this meeting, a freezing point. I was in frozen traffic
this morning. Or a futility point, where you can keep putting effort in
until there are decreasing marginal returns and it doesnt pay to put in
any more effort. These are all relevant thresholds to the topic at hand.

(Slide.)

What type of threshold are we talking about? Well, the proposed FDA
response by default allows antibiotic use, monitors resistance. When the
temperature, the resistance, gets high enough, its withdrawn, and after
resistance declines there may be some period in the future when the use
is permitted again. So this threshold, the policy threshold, is like a
thermostat, the set point on a thermostat, and in many ways it
guarantees that the temperature is going to hover around that set point.
It depends on how long you leave the heat off, of course, and how far
above the threshold you allow it to go. But it acts as that set point on
a thermostat.

(Slide.)

So we have a reasonable question of asking whether antibiotic resistance
behaves like temperature.

(Slide.)

I think the answer is, well, almost. Its important to recognize the
ways that temperature \-- that resistance doesnt act like temperature.
In particular, there are different kinds of resistance out there. Some
kind of resistance arises by point mutations that accumulate in
organisms. Other kind of resistance sort of have existed for a long time
in the organisms where the original antibiotic was discovered and there
are cassettes of genes, and Im thinking of the vancomycin resistance
cassette, that have a very ancient origin. These things can move around
among ecological reservoirs. If you talk to doctors and microbiologists,
they like a model something like this, that after you start to use an
antibiotic \-- and Im not being very specific about where. We could be
talking about on a farm or in a hospital. That there is some lag before
resistance first appears, and the drug is most useful during this period
of time. Then some kind of high-level resistance appears and starts to
spread, and the utility of the drug declines.

So this is not anything very fancy. Its just a general model, but if
this was like temperature it would start to go up immediately after you
started to use the drug. With high-level resistance theres a lag. With
resistance thats acquired by point mutations it may be very much like
temperatures, where the MICs start to raise over time and it gets hotter
and hotter. The time scales of these periods of time may be very
different. You may have several years before that first high-level
resistance drug appears or high-level resistance gene appears, and then
over several months it spreads.

(Slide.)

The point is that resistance, antibiotic resistance on these mobile
genetic elements spread epidemically. They dont spread like
temperature. So there may be a long time before high-level resistance
appears, and before that appears the drug has its maximum utility
because resistance is almost absence. Then after resistance appears, it
may spread rapidly and the utility of the drug declines rapidly.

As Im saying again, this could be on a farm and it could be in a
hospital, but it really depends on the mechanism of resistance. If were
talking about mobile genetic elements that can move around among
species, Im not making any claims about how frequently that happens.
Im just saying that they can move around among species. Then you have a
very different issue than if this resistance only accumulates by point
mutations.

The other thing I would like to make just before I move on is that
resistance responds to the level of antibiotic use, not just abuse. So
if youre heavily using a drug, even if it is appropriate, you are very
likely to get resistance in a short period of time.

(Slide.)

So why is all of relevant? Well, Im going to make a simplifying
distinction, and like all distinctions its somewhat artificial and only
if its useful should we use it. Youll have to decide whether you think
its useful. This is a distinction between a zoonotic organism and a
human commensal organism, and the differences are in the relative rate
at which these things get transmitted among humans after theyve been
acquired in a human. The zoonotic organisms are primarily acquired
directly from food, rarely spread from human to human. They ordinarily
dont colonize. Now, colonization and infection are different processes.
So they dont hang around for very long, and human commensals commonly
colonize humans, often without causing harm. They in fact can cause
disease under the appropriate conditions. So weve heard a lot about
this from doctors, who know more about it than I do. They are frequently
transmitted from human to human.

Now, one of the most important things that were finding out is that
there is some host specificity in these strains. So you can have a clone
which you could consider a chicken clone or a beef clone which doesnt
do very well when you put it in a human gut compared with human clones.
Okay? So it may be very hard for some of these strains to colonize
humans. This is not to say that you couldnt get a gene transfer event
if you have a mobile genetic element, but there the strain itself may
not colonize under ordinary conditions.

So we have this picture where there is a chicken population and a beef
population and a pork population and a human population, and movement
among these populations is more rare than movement of these clones
within a population. So rarely humans are colonized by animal strains
that acquire antibiotic resistance genes from animal strains. Okay?
Again, I think this is relatively rare, but the point is relatively
rare, relative to other things.

(Slide.)

We think about these things as having different kinds of risks. With the
zoonotic diseases, the risk is that you become infected with a resistant
zoonotic organism and then you cant treat it. With human commensals the
same risk exists, but there is also this addition risk that one of these
human commensal organisms acquires the resistance gene and starts to
spread and propagate within a human hospital. Now, what determines
whether or not this gene spreads within a human hospital is probably
whats happening in the hospital. That is the use of antibiotics, human
antibiotics by humans in the hospital. Its hospital infection control,
probably not as much determined by whether or not these people acquired
one of these resistance strains from food. Okay? So its very important
that you understand that Im claiming here that human use is mostly
responsible for human antibiotic resistance, and yet I still think its
significant. Okay?

(Slide.)

When we start to think about what happens with one bug and all of the
drugs, or several bugs and several drugs, we have this question of how
do we measure temperature which has come up. Its simple in a
one-drug-one-bug model, but the ecology of the human or animal gut is
incredibly complicated. Each drug may select for resistance in multiple
bugs. Other drugs can maintain resistance in a drug to a focal drug, and
the point is that any animal bug, zoonotic or commensal, might
potentially transfer high-level resistance genes to human commensal
bugs.

(Slide.)

So if you put everything together, we come up with this picture. That
period time when the use of an antibiotic is most critical, where it is
most useful because no high-level resistance has appeared, is sort of
the most important part of a drug \-- of a time you can use a drug. That
length of time is shortened if use a drug heavily in animals such that
its constantly introducing or exposing humans to these antibiotic
resistance genes. That eventually one of these will be acquired by a
human strain, a human commensal strain, and spread within a hospital and
become a problem; become endemic within a hospital because of human use,
but it happened when it did because of the use in animals.

So Im not claiming that it drives resistance in humans. Thats driven
by human use and human practices. But what I am claiming is that it
expands the animal reservoir of high-level resistance genes, it exposes
human populations to those resistance genes, and that it shortens that
length of time. Okay? So that years of utility in humans may be wasted.

(Slide.)

So I think in short that the zoonotic model is inappropriate for human
commensal organisms. I think it is very appropriate for a human zoonotic
organism, and in particular may not be appropriate for streptogramin
resistance in e. faecium. That threshold concept may be appropriate for
some kinds of quantitative resistance, but not for high-level resistance
that has not yet appeared in humans. That in order to preserve the new
drugs for humans

\-- you should preserve the new drugs for humans, but that after
clinically significant resistance appears in human Im suggesting that
at that point approving the drug for use in feed animals might be
essentially harmless. At that point I think a threshold concept may be
appropriate.

So we suggest that a different kind of trigger ought to be set for
commensal organisms or for drugs that can potentially be used to treat
human commensal organisms with high-level acquired resistance, which is
to set a trigger in humans. You dont allow the drug to be used in
animals until some point in the future when clinical significant
resistance appears. At that point, the existing FDA threshold may be
appropriate.

(Slide.)

I just want to point out that \-- I tried to tie this in earlier. I
should have brought it in earlier, but this was Marcs slide from
earlier. Im talking about the kinds of events on the far left of the
screen. I think Marcs introduction to this topic was very appropriate,
and that Im talking about the spread of resistance among human
commensals, the kinds of things you cant track very well. Why? Because
theyre rare events. They dont have to happen very often to have a very
big impact, and very hard to study scientifically or quantitatively.

(Slide.)

So our proposal for the drug-centered policy, and Ill end here, to
maximize the time that a drug is useful in humans. We set a human
threshold which acts as a trigger, not as a set point. Once we exceed
that threshold, we approve the drug for use in animals after clinically
significant resistance exceeds that threshold. Okay? At that point we
manage antibiotic resistance in zoonotic and commensal organisms that
were acquired directly from animals using the current proposal. Thank
you.

(Applause.)

***[Alternatives to Establishing Thresholds]{.underline}***

***By Dr. Tony Cox***

MODERATOR MITCHELL: Our next speaker will be Dr. Tony Cox, who is
President of Cox Associates, a Denver-based research training and
consulting company specializing in health safety and environmental risk
analysis. Hes Clinical Professor of Preventative Medicine and
Biometrics at the University of Colorado Health Sciences Center and
Professor of Mathematics at the University of Colorado in Denver. Hes
on the editorial board of [Risk Analysis]{.underline}, an international
journal, and is a founding coeditor of the [Journal of
Heuristics]{.underline}. Dr. Cox has authored an coauthored over 100
journal articles and book chapters on advanced aspects in these fields.
He has over a dozen U.S. and international patents on applications of
uncertainty analysis and optimizing and signal processing in
telecommunications.

Dr. Cox.

DR. COX: Thank you. I hope you dont mind my using transparencies. I
find them a little more flexible. Im never sure what Im going to say
until I see. You know that Ive been critical of the H=KQ framework. I
think it lacks foundation and perhaps lacks predictive value. In fact,
Im sure it lacks predictive value.

What CVM invited me to do was to propose an alternative. So if this is
not the right answer, what is? Originally I thought thats going to be a
tough topic to cover in 15 minutes. Then I thought actually its going
to be a very tough topic. But then it occurred to me, okay, theres a
lot of challenges here. Theres a lot of problems, and theyre
interesting problems having to do with non-traditional exposure and
non-traditional response.

(Slide.)

But I actually think that we take a textbook approach to risk analysis
\-- by the way, for folks who are not speed readers and who are annoyed
at my taking slides down, a super set of the slides that Im presenting
is at the back of the room. Sharon, I thank you for that. If we take a
textbook approach to risk analysis and risk management I think it may be
good enough for this complicated case. What I mean by a textbook case is
something like this. You being by saying, Well, what are the decisions
that Im trying to support? And you might enumerate even more
possibilities than ban or dont ban. You try to say within the province
which you are allowed to control, What can I control? Then for each
act that youre interested in, you do try to predict. You explicitly try
to predict what are the likely consequences of taking that act.

So for example, in the threshold model if you cross an action threshold
and the only action you can take, for example banning, would make things
worse instead of better because lets suppose that sick animals lead to
sick people as an example. Then simply being in a situation where a
threshold is passed doesnt tell you what action you want to take.

So instead Im just saying enumerate the actions that you are
considering, then quantify the probable consequences of each action, and
that is a predictive step. That is the part that Im going to focus on
for the rest of the talk. Then once youve done that quantification to
the best of your ability, pick the act that leads to the most preferred
probability \-- distribution of consequences and characterize the
uncertainty.

(Slide.)

So theres some post-processing steps saying once you know what to do,
do it. But the risk assessment part if how do you quantify the probably
exposure health consequences of each alternative act, and I think there
are two principles that are important and not especially unusual.

One is you relate acts to the frequency distribution in a population of
exposures, and by exposures here I mean relevant microbial load. So you
say, Look, if I ban this thing, if thats one of the acts that youre
considering, which I think it would be, If I ban this thing, how will
the frequency distribution of microbial load change in the populations
that Im trying to protect? If the answer is it would get worse or it
wouldnt change, that should be relevant in deciding what youre going
to do.

Then secondly, and also crucial I think, you have to take the population
frequency distribution of exposures, or Ill just say exposures for
short, and relate them to the probably adverse health consequences. This
is the root of my question from yesterday, which is, well, if exposure
doesnt have any bad consequences, would you want to regulate it? Again,
I think you have to take dose response information into account. You
have to say if somebody has a certain microbial load how likely is it
that there will be an adverse consequence. I would welcome the detail
saying, and what is that consequence? Is it death? Is it diarrhea? What
is it? So this I think is a very sort of ordinary framework. There is
room for some technical innovation. There is a particular modeling
technique, called a \-\-- event simulation which tries to follow
individual chickens in a computer, and eventually those chickens have a
misfortune and they become chicken meals. Then you follow the individual
meals and look at how those meals with their microbial loads interact
with people eating them, and some of those will pass an infective dose
threshold or go through a dose response model and cause a problem.

I think that framework for not treating all chicken as homogeneous or
all people as homogeneous is the very opposite of an H=KQ type
framework. Following the individuals gives a framework for incorporating
relative knowledge.

Now, I keenly appreciate what many people have pointed out, which is
that theres a lot of stuff you dont know. But I also think that
standard tools of risk assessment, namely conditional probability
calculations where you condition on what is known, and simulation, where
you model what you can model, does a great deal to reduce uncertainty
about the likely consequences of actions. I was intrigued. I think I
heard Dr. Tollefson say earlier today that if you introduce more facts
you introduce more uncertainty, and I thought, well, that cant be
right. I mean, facts are the things that get rid of uncertainty. Then I
could see how if you attached an uncertainty factor to

each fact you might get into trouble. But if you use conditioning, as in
conditional probabilities, or if you use simulation, then putting more
relevant knowledge in only constrains the true relationship between
input and output. Its never going to hurt you. So I dont think you
have to worry about that.

(Slide.)

Now, let me advocate four principles that I think should go into a
useful framework for predicting the likely consequences of actions and
therefore deciding what to do. The first principle is to include all
known important risk factors. Now, that sounds like it might take a few
100 years. But, again, if you understand Im just talking about
conditioning on whats known, these calculations are easy. Its what
risk analysts do.

Principle two, focus on probable causal relations. For example, if age
effects risk and if age is associated with other covariants, and if age
also effects exposure. For example, because younger people eat more
chicken or less chicken. Then when you get around to quantifying the
stuff in the box there, the relationship between exposure and risk,
youve got to back out the contributions of age. You have to back out
the indirect contributions through however many paths may be involved.

Now, there are old-fashioned techniques for doing that with names like
causal analysis and path analysis, and there are squeaky-clean new
techniques for doing it based on modern Bayesian causal graphs. This is
highfalutin stuff, but I think its essential if youre going to do
causal prediction, and I think again its not hard. These are not
methods that have not been published, or written up, or studied, or we
dont have extensive textbooks for how to do them. So I think lets
think causally and lets do causal modeling.

Principle three is to model the physical process that leads from acts to
their consequences. This is the direct opposite of an attributive
approach where you start off with historical or current measurements of
two variables where you dont know what the causal relationship between
those variables is and then start doing arithmetic attributable risk
calculations. Those who were awake earlier will know that I dont fully
agree with the attributable risk calculations that the CVM has done, but
you dont have to get into these philosophical perplexities of how to do
attributable risk calculations if what youre trying to do is to model
the physical process.

By physical process what do I mean? I mean the microbial load traveling
on a chicken as it goes along from farm, through truck, through
processing, through slaughter. I dont know why we would ever sample
outside the slaughtering plant, by the way, because the rest of the
causal process is suppose we irradiate the chicken or suppose we wipe
out the microbial load after the point of sampling; and I dont see any
mechanisms for automatically factoring that into K, but I know that if
you were modeling the whole process you would have to model what happens
next, all the way up to the point of cooking and consumption. Ive built
a model like this. It didnt take that long. Theres pretty good data
available for campylobacter, and I would be happy to share the details
of that.

I was trying to think of a quicker way and a more compelling way of
summarizing this idea, and based on Steve Sundlofs very helpful comment
that H=KQ was not supposed to be a predictive model I realize that if
what youre trying to do is to predict the impact of changes in action
on changes in health then a lot of my conceptual problems with this
model can be really simply expressed. Namely, its not true that if H=KQ
then change in health equals K times change

in Q. I think what I learned from Steve is, well, they never wanted to
do that. That was not CVMs intent. But I think in the useful model you
must do that, because I think that any useful model by definition is
useful because it tells you the likely consequences of action. It says,
If I change my actions, how will the consequences that I care about
change? So I do want something that gets me from delta Q to delta H.

Okay. Then Principle four is validate the model, both the whole model
\-- so try fitting it to some data sets where you know the right answer,
simulated data sets, and see if the model produces it. Then also
validate the modeling assumptions, each and every one of them. By
validation I dont mean anything too scary. I just mean make sure that
youre fitting the data.

(Slide.)

Okay. So let me give you a couple of examples. One is I recently got
some data from CDC, and I took a quick look at it for confounders and
theres some weird things about this data. This is from 1996 and 1997.
Its case control data. One thing I did was to look at what are the risk
factors. What are the things that are associated with whether youre a
case or whether youre a control? I strongly expected that chicken would
be one of them, but it isnt. The chicken variables are not
significantly associated with campylobacter at all, and in fact theres
several different chicken variables and they are non-significantly
negatively associated with campylobacter consumption. So that struck me
as pretty weird, but what was a powerful predictor of whether you were a
case or a control, the single most powerful predictor, is whether you
had eaten lettuce in a sandwich in a restaurant recently. In fact,
restaurant food, restaurant food consumption, turns out to be the single
most important predictor of case versus control status. Which is not at
all the way I expected going into this.

Well, digging around in that data a little bit more it turns out that
things like age \-- well, age specifically, is associated with a whole
bunch of things. Like going to restaurants less often and not having
kids in day care, and owning cats less often and preparing meals at home
more, which is a protective factor, and so on and so forth. Being less
accepting of undercooked burgers, sending them back I guess, washing
your hands more often. Our elderly population is to be admired. They
engage in protective behaviors and stay away from the at-risk ones. They
also eat less chicken, and they have exposure to raw chicken, and these
are all significant associations. And, by the way, they have reduced
risk of CP illness. Somebody said yesterday that we all know it to be
the fragile, elderly population is more at risk for campylobacter, but
theyre not. Theyre less at risk from campylobacter.

Now, the reason I point this out is if you think \-- if what you care
about is how would a change in Q change health impact, which is what I
care about, then you need to take out the effect of age. In fact, you
need to take out the effect of all these confounders in order to be left
with something that might be causal. So in the interest of time, let me
not go through how you remove confounders, although folks who are
familiar with, for example, multiple regression models will know that
you can control \-- you can try to partial out effects within even a
linear framework.

(Slide.)

Within the context of CVMs framework I do note that their estimate of
H(x) has a term, the proportion of total cases due to exposure to
animal-derived food commodity. As I suggested before, I dont think that
that phrase actually denotes anything. I think that there is not a
definition that goes with due to exposure. My favorite example is if
it takes being beaten by five bugs to get sick and four of them are
responsive to fluoroquinolone and one of them isnt, then how much of
the risk do you attribute to the one that isnt. I think that is a
policy question, but whether or not its a policy question \-- actually,
Im sure that it is. But whether or not you agree with that, at least
when you get to this point in the process you have to remove the effects
of confounders in order to go forward. So as we change this framework
from an attributive framework to a causal, predictive framework, it does
become important to take confounders into account.

(Slide.)

More generally, a short op ed piece is that any risk model that does not
control for confounders is not a correct epidemiological model and it
should not be called an epidemiological model. It should be called an
assumption. Just a small side comment.

(Slide.)

Okay. Principle two is quantify the probable causal relations between
actions, exposures, and adverse health consequences given other risk
factors. Let me not go over in any detail how to do that. I want to
focus in the few minutes I have left on what we should be trying to do,
and then it would be great to have a conversation about how should you
best do it.

(Slide.)

I will simply note that a straightforward approach is to try to look at
the changes in microbial load from stage to stage in a simulation model,
and I think that that approach can be made practical and can be backed
with data.

(Slide.)

Principle number three is to model the physical process and dont model
other things and include it as part of the answer, and let me show you
what Im driving at here. Suppose we have a slab of meat or a days
production of meat extending from the left to the right here, and
suppose that there are two colonies of campylobacter growing in that
meat thats been produced. I noted those two colonies by X. Supposed
that I take four samples. I divide the meat into four aliquots, and I
study them carefully and I find that two of my four samples contain
campylobacter, or more generally contain the bacterium of interest. Now,
suppose that without changing the physical situation at all I divided
that same days production of meat into eight samples instead of four.
What would happen to the proportion of samples that contains bacteria?
It would fall by a factor of two, right? Now again, suppose that I went
further. Much pleased with this result, I went on down and took 16
samples instead of two or eight.

(Slide.)

Now I would have two out of 16 as the sample proportion that contains
bacteria. Now, the reason I lead you through this entertaining and
instructive exercise is when I look at CVMs proposed calculation of Q
Im struck by the fact that we take the total pounds of product consumed
and we take the proportion of sampled pounds containing bacteria, but we
dont take any information about how much bacteria the samples
contained. I realize, you know, if instead of pounds you use tons, or if
instead of tons you used grams, you would greatly change Q even without
changing whats going on in the physical world at all.

Whats going on in the physical world is colony forming units per amount
of meat, which doesnt depend at all on how I apportion that meat in the
sampling. But this formula, because its trying to get after an
attributive question, how do we take this burden and attribute it back,
it uses non-physical constructs like proportion of sampled pounds
containing bacteria. Containing is actually not a physical concept. If a
slab of meat contains some bacteria, and I cut out the part of meat that
doesnt, I move it across the room, would I say that meat still contains
bacteria because it was part of a thing that did? This is the level of
philosophical difficulty that I think these formulas get into.

(Slide.)

Okay. So principle three is stick with the physical. Forget about the
philosophical. By the way, how to do that? Simulation again seems to me
to be a perfectly adequate and useful approach for this, and it has a
very great benefit. If you use simulation, \-\-- simulation, you can
quantify the likelihood that you get colonies bunched together.

Now, to me, because I believe in causal risk analysis, dose response
relations are essential. You cannot do useful risk analysis without
understanding the relationship between dose and response. What this
diagram is supposed to suggest is that it matters, even if we agree that
we should look at colony forming units per quantify of meat and not how
many samples and what proportion of samples contain an undisclosed
amount of bacteria. This bunching effect I think is really important for
health. Im sure that whats going on with chicken and
campylobacterosis, because most of us eat chicken and arent sick, Im
sure that whats going on is its not the average queue, its not the
average dose that matters. Its the right-hand tail that matters. Its
how many people are actually getting exposed to enough colonies in a
meal to make them sick.

If you believe that, then your thoughts about what the most rational
risk management steps to take will reflect a consumer for bunching. In
fact, it turns out that a small reduction in microbial load, for example
from following the USDA procedures, totally blows away the tiny little
fact of banning fluoroquinolone or completely removing it from the farm.
In terms not of what FDA cares about, which is attributable illness, but
in terms of what I think maybe they should care about, which is human
health, the number of case, number of days of excess illness.

(Slide.)

Okay. Last principle, validate the model. Dont ever use an invalidated
model, please. Let me give you an example of what Im driving at here.
This is picking up on a point that John Keeling made earlier today.
Suppose I have a simple data set with one, two, three, four, five points
\-- and by the way, on the X axis I have Q or something proportional to
it, because I know that there are factors, perhaps arrived at by
consensus, that can multiply these things. On the Y axis I have H, or
something proportional to it. It can be scaled by attribution factors.
Suppose that we looking at ingestion of Wheaties or something, or,
better yet, chicken with campylobacter. It turns out that low levels of
campylobacter may have a protective effect, come to think of it.

So supposed we plot these things, and heres data. Now, God help us, we
have to fit a model of a form H=KQ to these data. Now, what are we going
to do? The reason I appeal for divine aid is that H=KQ has to go through
00, and we began to discuss this early, saying why does it have to go
through 00, but thats the way the arithmetic works out. Set Q equal to
zero, and for any K what is H going to be? Its going to have to be
zero. So then you have the embarrassing situation where a change in Q
actually is a beneficial effect, but any possible model of the form H=KQ
is going to show a significant positive relationship. For those who
doubt, it is statistically significant. You actually get an R^2^ in a
statistically significant relationship, but what Im pointing out here
is that there is model misspecification or model uncertainty, and it
would be very simple to validate or to invalidate the assumption that
H=KQ.

(Slide.)

In fact, Ive done this with real data. I used some data from FoodNet. I
had do it by FoodNet catchment area, because I at that time didnt have
individual data, but I fit the H=KQ model, again ignoring K \-- Q and H
may each be scaled up and down. This is not going to change the point,
which is that negative relationship or zero relationship becomes
positive when you assume that H=KQ.

It turns out that the best-fitting regression line is slightly negative,
not significantly. The best-fitting CVM model has a positive
coefficient, as it must. Its the only thing that it can do. So again,
were in that embarrassing situation, this time with real data, where
you might be called upon to ban something just because you picked a
model that was forced to go through the origin.

Now, there is a simple solution to this problem. This not something that
requires much work. Just allow for an intercept. Just allow for the
possibility of an intercept in your regression model. If its not there,
then the true regression line will pass through the origin. I mean, the
data arent there to fool you. They are there to help you.

So what I think is the mathematical form here is a mathematical
straightjacket, the combined data that are trying to express themselves
in a way that would trigger inappropriate action.

(Slide.)

Okay. Im almost done. Just a couple of other perspectives on this
problem, and this is not the only problem of its type. Its just the
easiest one to explain. There are some other difficulties with the H=KQ
formula. So even if you are not bothered by this, there are other things
having to do with the difference between causal relations and
statistical relations that are I think even more compelling. But in this
case, basically your risk estimate is driven by where the data cloud is
with respect to the origin.

This is the difference between looking at levels versus looking at
changes and the impact that a change would have. It may be that for the
data cloud the relationship is negative. Meaning that if you change,
make an incremental change in Q, you would see an incremental reduction
in H. But the H=KQ model is not looking at deltas. Its looking at
something that depends essentially on where the background level is.

(Slide.)

What that means in turn, weve talked a little bit about, okay, is the
model good enough to use. For that matter, is my alternative model, a
simulation model or causal approach, is that good enough to use. I think
to decide whether something is good enough to use we should look at real
cases, and heres a case of two different factors. The red cloud has to
do with consumption of fried chicken, and the green cloud has to do with
how many times folks have eaten out in non-fast food restaurants in the
last five days. The thing thats important to me is that the
best-fitting regression line shows a positive relationship, and its
actually significant, between eating out in restaurants and getting
campylobacterosis. But it shows a negative relationship between fried
chicken consumption and campylobacterosis. Kentucky Fried Chicken
doesnt count. Thats a fast food restaurant.

Now, if you were deciding where to allocate societys resources I think
the answer would be you should allocate them to the green cloud. Because
even though its lower on the page, there could be a causal problem. We
havent yet factored out confounders and so forth, but there might be
something to worry about there. On the other hand, an attributive model
that goes through the origin is always going to have to come up with a
higher K-res for the higher data cloud than for the lower one. Therefore
it would lead to a misallocation of societys resources.

Thats the thing that I think needs to be taken into account. Its a
criterion for saying when is a model good enough. When can we finally
stop and say science has helped us to make a better decision? Its got
to actually lead to the right resources allocations. One does \-\--.

(Slide.)

Okay. I appreciate your attention on that. Im going to close. I have a
really fun point to make, but Im going to let it go. Summary, a
satisfactory framework, a useful framework. A framework that would lead
to better decisions and a better choice of actions for protecting public
health must quantify the risk of alternative actions. What I mean by
that in light of Dr. Sundlofs explanation I really mean predict when I
say quantify here. Predict the risks from alternative actions. Model the
effects of multiple risk factors and explanatory variables, including
confounders, which is a subset of risk factors.

Account for factor interactions. We didnt talk about that very
explicitly, but the idea of backing out the direct effect requires
accounting for factor interactions. In a one-factor model, H=KQ, one
explicit factor, mainly Q \-- and I dont know how many implicit ones in
K, but thats not going to allow you to get at factor interactions.

Point four, avoid untrue \-- well, avoid untrue assumptions. Thats such
generally good advice. More to the point, avoid untested assumptions
where you dont know whether theyre true or not or whether \-- you
dont know whether theyre true enough so that the data wouldnt refute
them. In short, test your assumptions.

Okay. Number five, quantify the frequency distributions of exposures and
risks, not the averages. The idea that average health response is
attributable to average exposure is just wrong. Its the right tail that
matters here. So you need that frequency distribution.

Finally, I think a good heuristic for achieving these other goals is to
build a model that is consistent with \-- ideally that mirrors, but that
is at least with the underlying physical process leading from acts to
probably consequences. I think if you want to follow that, youve got to
move away from an attributive framework, which doesnt care anything
about physical reality, and go more towards a simulation-based or a
causal modeling-based approach. Okay. So those are my recommendations.
Thanks for your attention.

(Applause.)

***[Public Comment Period]{.underline}***

***[Suggested Alternatives to Presented CVM Approach]{.underline}***

***Facilitated by Jim Heslin***

MODERATOR MITCHELL: Dr. Cox is coming back up and Jim Heslin will begin
facilitating the open comment period.

FACILITATOR HESLIN: Thank you. Were into the last open comment period.
So if anyone wants to step forward to begin.

DR. KATZPER: Yes. I took a little bit of a break before this session. I
made an overhead which Id like to show. Do I have permission?

FACILITATOR HESLIN: Im sorry. You took a break?

VOICE: He has an overhead and he wants to show it.

DR. KATZPER: (Away from a microphone.) I went by the FDA Welcome Center
and made an overhead. Okay. I find that I agree totally with the last
speaker because I am a modeler myself, and I think FDA is correct and
has gotten a lot of good advice in trying to do the modeling, but
theyre wrong in terms of where their focus \-\-- is. I dont care how
much meat people ate. I really dont care. What I care is are the
bacteria resistant \-\--. If there is bacteria and if there is a
contamination in the food supply lets assume that leads to a disease
outburst. The Department of Agriculture is in charge of inspection and
regulation. FDA is not. CDC is responsible to ascertain what is the
cause, and sometimes its really hard to find out. In all this the FDA,
by examining food supplies, can determine what is the resistance level
of bacteria. This happens through the gathering of facts. It doesnt
have to do with anything else. With these facts afterwards I can
conceivably make the model.

There is another aspect of the FDAs possible intervention, and that is
discovering what percentage of the patients have resistance rates. It
doesnt have to do with anything about quantity. Really, this is so
misguided, but if there is resistance in 30 percent \-- and its a
little more complicated now, but if there is a resistant bacteria in 40
percent of the population or 50 percent of those people who have gotten
this infection you are in trouble. Between these two facts we can
determine where do we have to put some sort of decision making.
Arbitrary, arbitrary, never mind the casual relationship, on \-- no,
really. There is an overview that if you are eating food and you get an
infection, that infection can either be resistant or not resistant. If
its not resistant, the Department of Agriculture is at fault. They have
to get their regulations in order. If it is resistant, FDA is as fault
because FDA is responsible for the drugs that are creating the
resistance. So that FDA has to determine these two things. Having
determined these two things one can look at these facts and determine
whether there is a relationship, what the relationship, what the
arbitrary decision should be, because it will be an arbitrary decision
in any case, even with the best facts and the best modeling. But with
the best facts they need these two things, and with modeling youll be
able make reasonable arguments that intelligent people can accept.
Thats what I hope the CVM \-- is the direction that the CVM is going.
Thank you.

FACILITATOR HESLIN: Im sorry. Could you introduce yourself and the
organization youre with?

DR. KATZPER: Oh. My name is Meyer Katzper. Im

\-\--. Im a modeler really.

FACILITATOR HESLIN: Okay. Yes?

MS. BARTHOLOMEW: Im Mary Bartholomew, and I have a question or two for
Dr. Cox. I was curious about the graph in which you show trying to
validate the H=KQ model using FoodNet data, and in particular what
FoodNet data and what did you do with it. Because in our relationship we
have backed out resistance that was due to travelers and resistance that
was due to having received prior fluoroquinolone use as a human
medicine, and I dont know that theres anything in what you have taken
from the FoodNet data that mimics that.

DR. COX: Yes. Thanks for the question. The point about negative versus
positive slopes Ive actually done with two different data sets. The one
I showed, because its so readily publicly available, is based on food
catchment area data only, not adjusted for anything. Its just aggregate
numbers, aggregate numbers.

However, I have received from CDC by a more laborious route individual
level data, and for those individual level data I have been able to take
out the travelers and medical recipients of fluoroquinolones. In that
data set, too, although I didnt show it, it continues to be the case
that chicken is not significantly associated when you take other factors
into account with campylobacter case control status.

Now, this is 96 and 97 data. Somehow Ive not been able to obtain yet
from CDC more recent data, and I was very \-- the point that Im making
here actually is not primarily intended to be a point about the real
world. Its primarily intended to be a point about methodology, but I
think the example actually works with real data, and I would be very
happy to share data with you so we can make sure thats true.

MS. BARTHOLOMEW: Okay. Then a second point that I had was about
estimating the infective dose and \-- not really the infective dose, but
the probability of having recieved an infective dose and if its not
true that the number of cases of people who have illness as a function
of total population might not be our best estimate of the probability of
receiving an infective dose.

DR. COX: I guess the most relevant response would be probability under
what condition. The model I want to build would say if we were to reduce
microbial load by a factor of two, how would risk in the population
change? Would we see half as many people sick? Would we see more than
half as many people sick? Or would we see fewer than half as many people
sick?

The intriquing slide that I mention and forbode to show demonstrates
that if we use CVMs recommended approach based on the campylobacter
study the answer to the question is cutting average microbial load in
half reduces risk by less than a factor of two. Thats using the CVM log
exponential model.

If instead we assume, as I have, that microbial load is determined by
taking initial microbial load and amplifying or reducing it by a number
of processes, and if that leads to a logged normal distribution, then
its the case that cutting microbial load by a factor of two, average
microbial load, reduces risk by much greater than a factor of two. So I
think there is important modeling uncertainty, namely why choose log
exponential versus log normal versus something else that dramatically
impacts the predicted impact of changing microbial load, and my whole
attention is on how will risk change if microbial load or some actions
that affect it change.

MS. BARTHOLOMEW: Thank you.

DR. COX: Thanks.

FACILITATOR HESLIN: Thank you. Yes?

DR. ANGULO: Fred Angulo from the chief of FoodNet and NARMS, the
Foodborne Diseases Branch of the CDC. I just wanted because Im unsure
of the data that weve sent you or that you have, may have gotten
through some of our partner sites. But, of course, NARMS started in 1996
and FoodNet started also in 1996, and we did \-- I believe the data set
that you have is in 1996 and 1997 we did the first cycle of our
population survey, which is a random, Digidial telephone survey of 150
people in every site in which we ask \-- and at that time in 1996 there
were five sites and we did it for 12 months.

During that period of time, we picked a random member of a household and
asked them a whole variety of questions, which included exposure
questions which included In the last five days what foods have you
eaten? Which did include, And also, where you ate them? Which would
include eating them outside the home and eating them inside the home. So
this was a population survey information. Those data are available on
the web on what we call our Exposure Atlas.

During the same time period, of course, we were running NARMS, and we
have information about human, and we also run in FoodNet, in which we
ascertained all culture-confirmed illness. So we had another data set of
human illness, which was FoodNet, and then we also have another data set
on resistance information, which is NARMS. But it would be ecological to
associate the regional differences in resistance in that NARMS
population, which is available on the web also, with the differences in
exposures. That is differences in prevalences of eating chicken outside
the home, et cetera, which is evident in our population survey.

So all the data that you just presented I believe is an ecological
analysis of two data sets.

There is another study that we have done, which you did express some
frustration that you dont yet have that data set to explore yourself.
We are in the midst of that analysis. We have published preliminary
findings of that analysis. We call that our case control study, and in
that study we ascertained ill people. We also determined if they had a
resistant infection or not, and then we got random Digidial controls
that were matched to those people and did an epidemiological study which
was not ecological and then determined risk factors for those.

We are in the midst of doing further analysis of that. Preliminary
findings of that were presented by

Dr. Freedman in one study, Dr. Kassenbaum in another study, Dr. Marano
in third study, all this last year at an international meeting at CDC.
All of which have somewhat different conclusions than what you presented
in your ecological analysis.

Finally, the last thing that is not yet known that perhaps youre not as
well aware of is that of course we have taken those univariant analyses
from our epidemiological study to multivariant analyses, and we are in
the midst of doing that multivariant analysis. All of which I believe
our findings will be contrary to some of the findings that you presented
in your ecological analysis.

DR. COX: Great. Thank you. A couple of comments. First, I believe that
what I have is not just the ecological data, and specifically what you
sent me, what CDC sent me, was 9,000 and some cases with a questionnaire
that indicated that these were from a case control study, but that it
shuffled together pediatric and adult cases, cases and controls. I
desperately need to get your key, although Ive been able to pretty much
unshuffle those files by looking at which questions were answered.

DR. ANGULO: Dr. Cox, those 9,000 people were \-- that was not a case
control study. That was randomly selected people regardless of illness.
It was a population survey.

DR. COX: Okay.

DR. ANGULO: We did ascertain illness in those people, but the illness
was, In the last 30 days have you

been ill with diarrhea? The exposure questions were, In the last five
days, what did you eat? We have not attempted to any way do an analysis
of diarrheal illness of those people because the time frames are very
different. We did that analysis in a different study design, which was a
case control study design, not a random population survey.

DR. COX: Or the reverse. To take your questions, in either case, and
again not being sure exactly what I had, I was aware that it was
individual-level data as opposed to ecological data.

Secondly, the ecological fallacy to which you refer, which is the whats
true at the group level need not be true for any individual in the
group, can never happen if one particular assumption is made. That
assumption can be expressed as H=KQ. Under those conditions, in other
words under the conditions assumed by CVM, aggregation works perfectly
and the ecological fallacy doesnt arise.

However, thirdly, my point in showing the possibility of negative slope
progression line through admittedly catchment area data, as I was
careful to specify, is not to make an assertion yet about the real
world. Its to assert that the methodology that assumes a positive
relationship even for negative data is flawed, and that point can be
illustrated even with hypothetical data. So that I dont require a
resolution to the ecological versus non-ecological finding.

Fourthly, your comment that the three investigators who published on
this technique have reached substantially different conclusions, Im
more impressed by the substantial overlap. Freedmans study in
particular I think correctly points out, as other peer-reviewed articles
have, the protective effects of domestically-prepared chicken. He didnt
say, but its evident from the data, that older people who have prepared
chicken for a long time have a greater protective effect than others.

Now Im stumbling into conjectures about the real world, and in a way I
dont want to go there. The methodological point is if you have negative
relationships in the data, you shouldnt be fitting a positive model to
those data. That really can stand without clearing up where the data
came from.

FACILITATOR HESLIN: Thank you.

DR. GOLDBERG: Im Rebecca Goldberg. Im trained as a biologist and
statistician. Im with Environmental Defense, but Id like to offer a
non-technical comment for a change. That is to just offer some
clarification to the characterization of the NGO position that was
presented by Mr. Carnevale, who, as I heard it, and I may be
mischaracterizing him, said that the NGO community is essentially
opposed to the use of thresholds.

It is true that we have concerns about the use of thresholds. They
clearly offer a lot of challenges to FDA to implement in a way thats
sensible, and from the perspective at least of my organization it is not
wise to use thresholds as the first line of defense when it comes to
making decisions about the use of medically-important animal drugs in
animal agriculture. That said, there certainly will be decisions that
FDA has to make where the threshold methodology may be quite useful, and
we are fully supportive of FDA on going forward, and exploring and
trying to implement a threshold methodology. Thank you.

FACILITATOR HESLIN: Thank you. Are there other questions or comments?

DR. WILSON: Jim Wilson for Resources for the Future, and I hope you will
forgive me if I do a little bit of thinking on my feet. But it seems to
me that there are some lessons that could be learned by bringing
together each of the three proposals that have been made, and what I
have been thinking about is this. First, as Tony Cox started off by
saying, what CVM needs to do is explain to the public first what is its
policy goal. What are you trying to achieve by this?

The Center may understand this in great depth, but it clearly was not
something that was easily understood from the threshold document. So
when you revise it, I suggest you make that clear. I might assume that
the policy is to maximize the utility of antimicrobials in both human
and veterinary medicine. If that or something like that is the policy
goal, there are some ways that you might achieve it by more complex and
perhaps less litigious regulatory policy approaches.

First, I have a lot of admiration for the proposal that new \-- for the
trigger proposal that new antibiotics in human medicine not be approved
for use in veterinary medicine until resistance has started to appear.
The theory thats behind that is that by delaying appearance of
resistance you wont prohibit, you wont forever delay it, but you can
delay it for some period of time and thereby maximize the utility of the
human drug. If that is the proposal, I mean if that is something that
the FDA can do without legislation, then FDA ought to consider that.

Second, then the management of resistance in antibiotics, use in
antibiotics in animal medicine is something that probably could be
approached more simply than this threshold proposal. Im not persuaded
that CVM can use any risk \-- any explicitly risk-based formula to
identify and acceptable threshold. Im persuaded that as far as the
public is concerned the only risk that is acceptable is no risk. I think
that one could approach this by saying if its a risk and we cant find
it, thats equivalent to no risk, and certainly the Agency does that in
a lot of cases.

If you start from that as a presumption and you recognize that the data
that are available to the Agency are in fact a time series of resistance
in the various bacteria of interest and that there is some variability
in those data so that you can establish a background data with variance
around some means, I would think that the Agency would take as a trigger
point for dealing with zoonotic resistance the reliable appearance of an
increase in resistant organisms in the animal population. Ideally the
Agency would like to have that data broken down by area, if not
individual farm, so that the actions that the Agency could then take
would be more like, oh, Dr. Carnevale described for the dealings \--
with the way it deals with exceedences of tolerances in drug levels in
the meat and so on.

FACILITATOR HESLIN: You have about a minute left.

DR. WILSON: Deal with the problems on a regional or individual farm
level rather than taking the broader action. I dont know obviously if
the Agency has contemplated something of this kind, but at least it
seems to me this might be a way to synthesize the suggestions that our
panelists have made.

FACILITATOR HESLIN: Okay. Thank you. Yes?

DR. BARLAM: Hi. Im Tamar Barlam. Im a physician, infectious disease
trained. I work with the Center for Science in the Public Interest. As
were winding down here it seemed like a good time to say some nice
things to the FDA and the CVM. So first, rather than John likening it to
a lamp with a drunk underneath it, I would like to maybe liken it more
to lighting the candle rather than cursing the darkness.

We support the principle that CVM is trying to accomplish here, although
obviously acknowledge that theres a lot of need for refinement and
input. We certainly support the standard of the reasonable certainty of
no harm. We very much support the industry providing more accurate data,
especially with some criticisms of the recent UCS report. It would be
nice to have very good drug use data as well to expand the NARMS program
and continue to have good surveillance data.

I guess I wanted to end as an M.D. slightly reinterpreting the
Hippocratic oath that was mentioned earlier. The way that I interpret
that oath that I took quite a few years ago about above all do no harm
is not to continue to use such an extensive array of antibiotics without
worry about the public health harm, but to take rather the more cautious
approach and above all do no harm is not to put the public health at
risk. Thank you.

FACILITATOR HESLIN: Thank you.

DR. CONDON: Robert Condon from RJC Associates. I want to thank CVM for
the first step. The Chinese proverb that a journey of a 1,000 miles
starts with an initial step, thats one of the biggest hurdles is taking
that initial step and thats whats happened. Were not sure which
direction its gone, but the key thing is you got to take that step.
That stimulates discussion. That stimulates different ideas. Its the
beginning of the process.

What needs to be done is to evaluate whats happened, the different
comments. Its going to take another step, more discussion. I think the
thing CVM needs to be aware of and keep in mind, this is not something
thats done in a year or two. I think to get to a good solution and a
good, sound proposal youre looking at five or six years. I think thats
realistic.

From my other favorite philosopher, Yogi Berra, this is like déjà vu all
over again. If we change about 10 words, this is the same arguments that
we had with regulating carcinogens, the sensitive of the method document
back in 72. A lot of the same issues were there, the outside influences
and things. The think that youve got to keep in mind and the goal is
whats going to prevail is sound science, and youve got to keep getting
to that goal of sound science. Theres a lot of pride in authorship, and
every time we put a proposal out and you get comments on it, you just
say, Well, why are they making those comments? You know, we got it
right. But then when you sit down and evaluate we ended up adopting a
lot of things that came in as comments. But the thing to keep in mind
and where that journeys got to go to is sound science. Thats the only
thing that lasts, and its the only thing thats going to keep things on
track.

FACILITATOR HESLIN: Thank you. Could we stay over here, since youve
been waiting?

DR. MELLON: Margaret Mellon from the Union of Concerned Scientists. I,
too, would like to congratulate the FDA for having taken the first step
that its taken. Ive heard all the arguments. I understand that we do
\-- that there are many challenges before the Agency before it can
refine the model to the point that its going to work, but it is \--
from the point of view of the public health community it is a first step
to be \-- it is to be applauded.

It is to be applauded because we do want new veterinary drugs; and,
unless we can come up with a process that we all have confidence in,
there will be no new drugs. Not just for the next five years, which I
think is a frame where everyone \-- a kind of time frame that everyone
agrees will see no new drugs, but well beyond that. So this is effort
well spent for all of us, but I would think particularly for the
industry. If you want new drugs knocking this down, which is all too
easy, wont get you or I think all of us where we want to go.

My second point is to join with AHI in calling for a significant
strengthening of NARMS. I mean, its really just too small. It doesnt
offer the statistical robustness that we want, that we need, that I
really think lots and lots of people in the public expected us to
already have. They cant believe we only started doing this kind of
national surveillance only three years ago. So moving ahead with that I
think is a goal that I would assume all the stakeholders agree on that
we ought to put our shoulders to the wheel to get the folks who are
doing it the money they need to do it at a far \-- you know, at the kind
of next level of effort.

My third point I will admit does come out of \-- like a lot of people
here, I, too, have worked in kind of the policy arena for a long time.
So I remember a lot of the debate through the 70s and the 80s about
carcinogens, about pollution, about whether we \-- you know, whether we
can do it or not. I remember lots of people in various industries saying
we could never clean up pollution because it would cost too much and it
would put us all out of business, and, you know, the experience is that
once industry has a charge to do something that they can do it. They can
do it.

It doesnt put them out of business, and that does echo in my mind as I
make the next comment. That is that risk assessment, as wonderful as it
is as weve just seen illustrated, can be just immensely complex.
Anytime you dip your toe in that pond you come out wanting a simpler
solution to the problem ahead of us. So while I think risk assessment
may be the only way to go in the approval of new drugs, Im more
concerned, as I said before, about relooking at our existing \-- at all
the existing approvals of drugs, and Id like to suggest there that
there might be a simpler, albeit admittedly imperfect, way to go about
it. That is to just admit that you dont lose drugs as a result of
resistance unless you use them.

That means one, straightforward way of approaching the problem of
resistance is to minimize unnecessary use. I would just like to
challenge the animal drug industry, certainly the animal production
industry, to accept the challenge of redoing their system so that they
use less, and that, again, the most straightforward way of assessing
whether they have achieved that goal is to just look at quantity of use
and that a measure \-- that just using quantity of use to show that they
have demonstrably and substantially reduced use would be a large step in
a direction that may avoid some of the complexity weve seen here as a
way to solve this societal problem. Thank you.

FACILITATOR HESLIN: Okay. Thank you. Yes, sir?

DR. MARTIN: Thomas Martin from the University of Illinois. I have a
comment that probably refers more to the panel of the previous session
about the threshold method. If the threshold is going to be set based on
estimating what is the risk for public health from the use of
antimicrobials in animals I guess that if that estimation is accurate we
are fine. If we underestimate that risk, then the threshold could be too
high and perhaps we could still have a public health problem that were
not seeing. On the other hand, if that threshold is overestimated and
all the risk is overestimated and the threshold is too small, we could
be withdrawing drugs for animal use without that having a benefit in
human health.

So my question I guess is whether \-- is there any consideration or plan
from FDA or any other organisms after these discussions in terms of
assessing once the threshold method is implementing assessing what is
the positive impact in human health? I know that it is much easier to
ask for this than to do it, but I would like to know if that is going to
receive some consideration.

FACILITATOR HESLIN: The question was to FDA?

DR. MARTIN: Yes.

DR. TOLLEFSON: If I could rephrase the question so that Im sure I
understood what you meant. Is what youre saying is that we could error
two ways? One way would be not being protective enough of public health,
and the other way being overly protecting and therefore too much of a
burden on animal health; and because of that risk, are you going to be
sure, or is FDA going to try, to look at the human health impact from
whatever action we take?

DR. MARTIN: Right.

DR. TOLLEFSON: Yes. The simple answer or the short answer is yes. What
we intend to do is continue to monitor the impact on human health on
antimicrobial resistance, that fraction that we believe is coming from
animal food. In fact, I guess I think there is also a misconception here
that our actions are very final and theres no \-- theres a point of no
return. Dr. Carnevale pointed out that you really cant get rid of the
due process rights. So there is a time period, and there will be plenty
of opportunity to reevaluate that data and to look at what were trying
to do and what happens. What were hoping is a lot of this will be done
cooperatively with the animal producers and some of the prudent use
guidelines, for example, that sort of thing. So we can monitor what
happens in those case and look at the impact on human health. Does that
\--?

DR. MARTIN: Yes.

DR. TOLLEFSON: Okay.

DR. MARTIN: Thank you.

FACILITATOR HESLIN: Thank you. Yes?

DR. VOGEL: Im Lyle Vogel with the American Veterinary Medical
Association. Id like to join the others in thanking the FDA for hosting
this meeting. I think we have heard a lot of valuable comments from our
guest speakers and from the public during the open comment period. I
just wish that we could have had this discussion and this meeting prior
to the FDA taking its action to withdraw the poultry fluoroquinolones. I
think the Agency could have benefited from the type of discussions that
went on here.

During this meeting weve heard expression of support for the threshold
document. Weve also heard opposition, objections, concerns and
questions, and we hope that those concerns and questions can be
adequately addressed as the Agency moves forward. The AVMA has pledged
to support and assist the FDA in developing a science-based framework
document that does not unfairly penalize the practice of veterinary
medicine or the health and welfare of the animals under our care, and we
will continue to work with the FDA in that effort.

We will also continue our efforts to address our responsibilities for
judicious use of antimicrobials. We have invested too much of our
members dues money and the time of our volunteers who serve on our
policy-making and implementing committees to quit now. Were going to go
forward with that effort. Weve invested almost three years in that
effort up to this point. We appreciate the advice and assistance that
weve gotten from CDC, the Infectious Disease Society of America, and
the FDA in that effort. So far weve developed judicious use principles,
general principles, specific principles that apply to swine, poultry,
and cattle, other judicious use principles for K-9, feline, and equine
are in the process and will hopefully soon be approved.

With the assistance of FDA weve developed educational materials for
veterinarians, veterinary students, and the general public as well
producers, and those are being produced and distributed. You heard of
the support that AVMA has provided for the Veterinary Antimicrobial
Decision Support System that Dr. Apley mentioned on the first day, and I
think that will be a very valuable project to us in the future. The AVMA
has also initiated a project to develop a surveillance system to look at
the resistance in animal pathogens.

So weve got a number of projects going forward that I think will help
us solve this problem of resistance that are emanating from the
therapeutic use of antimicrobials in animals. So, again, thank the FDA
for hosting this meeting. We do pledge to continue our support. Thank
you.

FACILITATOR HESLIN: Thank you. Yes?

DR. KEELING: John Keeling, Animal Health Institute. I have got a very
quick question. I would be interested in the panels answer to the
gentleman from Illinois question. The question previous, relative to
how CVM was going to utilize the ability to monitor their actions in
terms of the result.

DR. CARNEVALE: I dont think any of us have any answers to that. I
havent really thought that far ahead, John. Excuse me, to be honest
with you, were still \-- you know, were still trying to get through
this phase of it. So I dont have any particular thoughts on that.

DR. KEELING: Tony?

DR. COX: I dont know either. I imagine it would be a good topic for
discussion. Id like to, if I may, make a comment. Which is first to
join many others who thanked CVM for creating this opportunity. I
especially thank CVM for asking me to try to come up with a different
framework, and Im sensitive to Margaret Millers comment that whenever
you dip your toe in the risk analysis water you are aware of the
presence of sharks. You wish for something simpler. Because what I have
recommended to CVM is that we basically take what I consider to be an
ordinary risk analysis approach, mean one that focuses on cause and
effect and on prediction and so forth, Id like to just put in the
comment that I think that this problem is of moderate complexity
compared to other risk analysis problems that Ive seen. I dont think
its as complex as the Wash 1400 risk analysis study that got the whole
field started years ago.

I thing that the intuition that folks from CVM and others have
expressed, which is that you dont have to model everything in order to
model something usefully and to make better decisions, I think that
intuition is right on target. Even though this is not the best forum and
is not an appropriate forum for going into the technical details, I urge
folks who want to do a good job with this and help make better decisions
to at least try the risk analysis approach, and I think well find that
its not so very complex after all.

DR. SMITH: Id just like to add that if the FDA takes Tonys
recommendation and makes a predictive model that NARMS data can be used
to validate and verify the model after the withdrawal of the drug to see
whether the policy actually has an effect.

FACILITATOR HESLIN: Okay. Thank you. Okay. Go ahead.

DR. ANGULO: Fred Angulo from Centers for Disease Control, and I wanted
to urge all the stakeholders to review the Interagency Task Force on
Combating Antibiotic Resistance in the United States. As most of you
are, the interagency task force was made up of 10 different governmental
agencies with lots of opportunities for input, and that group did come
up with a top priority item.

It is significant that implementing the framework process was judged as
one of the top priority items; and the task force went even further
because it is such an urgent public health situation to urge
implementation of the framework within two years, as all top priority
items were judged to be urgent enough to be implemented or have
significant progress in within two years.

So along those lines, why is it urgent that we implement it within a
reasonable period of time, within this two-year period? It perhaps
wasnt highlighted at the initial presentation by Dr. Morris, but he did
provide preliminary NARMS human data from the year 2000, and that was
for those drugs that are used clinically to treat persons with invasive
salmonella infection. That is third-generation cephalosporins for
children and fluoroquinolones for adults, and the data that he showed
was that in year 2000 that we have two percent of salmonella isolates
with a decreased susceptibility to ceftriaxone. Many of those are likely
to be resistance to ceftriaxone, and two percent of salmonella had a
decreased susceptibility to fluoroquinolone. Both of which suggests, and
both are remarkably higher than the previous year, both of which
demonstrate the urgency that we implement something in the near term for
at least those two clinically important drugs.

So the final comment is I urge the FDA implement a threshold on those
two clinically important classes of drugs in the near term, and we can
have additional discussions about thresholds for other drugs, other
processes, et cetera. But we are going to approach a threshold in the
near term that was probably going to exceed the reasonable certainty of
no harm. Lets set that threshold in the near term before it is too
late.

FACILITATOR HESLIN: Thank you. Yes?

DR. RANKIN: My name is Shelley Rankin from the University of
Pennsylvania, and Id just like to pick up on the point that Freds just
made about ciprofloxicin resistance in salmonella. We have a salmonella
reference center thats funded in part by the Department of Agriculture
in Pennsylvania. We have 10.5 thousand salmonella isolates in our
collection, and there are two that have ciprofloxicin resistance. We
have 104 that show reduced susceptibility to ciprofloxicin, but those
figures are still significantly less than the human figures. I just
wanted to make that point because the veterinary profession is getting
blamed for, you know, high levels of resistance, but we just dont see
it, and Ive published similar figures before from the Scottish
salmonella reference center that show that the incidence of
ciprofloxicin resistance in veterinary isolates is much less than in
human isolates.

FACILITATOR HESLIN: Okay. Thank you. Any other comments or questions?
All right. Thank you very much.

MODERATOR MITCHELL: Please join me in thanking the panel. Give them a
round of applause.

(Applause.)

***[Closing Comments: Summary, Concluding Remarks, and next
Steps]{.underline}***

***By Dr. Stephen Sundlof***

MODERATOR MITCHELL: The panel members are going to take more comfortable
chairs here. Im sure they are. We are about to have the wrap-up session
here, closing comments, summary, and concluding remarks and next steps
by Dr. Sundlof.

DR. SUNDLOF: All right. Well, thank you, everybody. I want to especially
thank the speakers for doing such an outstanding job in presenting some
very thoughtful talks. It will take us Im sure some time to try and
absorb all of the information that was provided, and I look forward to
that. I think this is a very stimulating discussion. I also want to
thank the moderators for keeping us on time. We are doing very well, and
I will try and do my best to maintain that tradition. So well talk
about some of the things that we heard at this meeting and some of the
next step, and Ill just tell you that Im reading this for the first
time, too.

(Slide.)

Summary from Session 2. There was no summary from Session 1, because
that was just bureaucrats. The general agreement from \-- there appears
to be some general agreement from Session 2 that appropriate \-- its
appropriate to consider certain drugs as higher priorities or a greater
risk of being lost to human medicine, and so I think there was a high
degree of trying to determine what those \-- what are the criteria for
judging those concerns. I think we heard both sides, that drugs that are
not being used much anymore because \-- in human medicine because of
resistance are less important than them. We heard the other side that
maybe theyre more important. So certainly its an area that needs to
have some further discussion.

There was interest in meeting with CVM to discuss methodology for
collecting needed drug use data, and I think there was a good discussion
on that. We heard the message very loud and clear that we need
additional information about the amount of drug use, where its used,
how its being used, for what purposes in what animals, those kinds of
things, and I think it was also mentioned that CVM is in the process of
developing proposed regulations that will address some of those issues.

(Slide.)

In Session 3 there was agreement I think that CVM should address the
complexities of more complex issues, such as indirect transfer of
resistance, environmental dissemination and multi-drug resistance. I
think theres agreement, and I think in my initial remarks I indicated
that, while we understand that these are important, it may be best to
not try and take on the entire complex problem all at once, but start
with something simpler like zoetic \-- zoonotic pathogens, enteric
pathogens like weve talked about.

The threshold approach should focus on treatment failures and not split
impact into enteric and systemic illness. I think that there was not a
whole lot of discussion on that, but I think there were those comments
that why have two separate thresholds, why not combine that into one. I
think theres a lot of different permeations that can be applied to
this, and I think thats a subject for further discussions.

There may be other impacts that should be consider as endpoints, such as
potentiation of transmission. We heard that there may be other
measurable objective endpoints that can be considered. I think were
open to considering any endpoints that again are measurable. Thats the
hard part, is trying to measure some of these endpoints in a consistent
and objective manner, but were certainly open to considering other
endpoints than the ones that weve considered.

We should address the variability of risk across the population, that
certain subpopulations may be more at risk than others. We heard a
little bit of discussion this afternoon about \-- especially about the
elderly, that they may be less at risk. So looking specifically, though,
at subpopulations from all the data was suggested, and I think thats a
good idea.

Threshold approach should be science-based while recognized that
societal concerns and values are important in developing risk management
and risk communication strategies. I think our risk communication and
risk analysis that we heard yesterday talked about some of the things.
The science should be used in the risk analysis \-- or in the risk
assessment part. That other concerns in taking risk management decisions
where you have options, risk management options, such things as societal
values and things, may be important in deciding which of the risk
management options you take and how you communicate that risk to the
public.

(Slide.)

Session 4, we heard that the model was too simple and must address the
complexities of all contributing risk factors. I think there was not
universal agreement on that area, but we did hear that from a number of
people.

There was agreement that there must be a mechanism to reassess and
periodically update the threshold. Comments were made on several
occasions that antimicrobial resistance is dynamic. Its a dynamic event
unlike residues, which are relatively static. So rather than having a
static approach as we do with residues, we may want to look at something
that is a bit more dynamic.

Need to evaluate whether the model that weve developed, which was again
largely based on our previous risk assessment model in campylobacter,
whether that might \-- whether indeed that is applicable to other
antimicrobials and other bacteria. I think thats a fair statement, a
fair question to ask.

We need to develop criteria to determine whether the threshold is
exceeded, not just background fluctuation. In other words, have we
really statistically exceed the threshold, or are we in the area where
were seeing noise and natural variation in the system that may have
temporarily exceed the established threshold.

Concerns were expressed as to whether a linear model was appropriate,
and I think we had a lot good discussion about that. In fact, we heard
some different discussion about where the intercept of those models
should be and whether the slope was going in the right direction.

(Slide.)

In Session 5 we had an expert panel to assist CVM in developing or
evaluating the NARMS data and recommend appropriate actions. I think we
heard that as one possible way of dealing with this issue other than
moving forward with the threshold approach.

We heard that emphasizing prudent use strategies was certainly
appropriate and may certainly help to mitigate some of the potential
problems associated with overuse or misuse of drugs, both in the human
community and in the animal community. We heard from the American
Veterinary Medical Association about the programs that they have in
place for making sure that their constituents, the veterinary community,
is \-- has available to it information, relevant information that they
can use to make sure that they are using drugs in a judicious manner.

For commensals, an interesting approach. We heard that for commensals
that you restrict the use in animals until the human resistance trigger
has been exceeded and then approve it. So in effect just the opposite of
what CVM was proposing, but a different type of situation where you have
commensals and potential indirect transfer of resistance elements from
animal commensals to human commensals.

Then develop a model that can address all known risk factors and the
impact of all risk management interventions, and we had an example of a
model, and its going to take me some time to absorb all that. But
certainly well be making for some interesting evenings, I can tell you,
in the next week or so, and to validate the model based on real data
that were collecting from the NARMS system.

(Slide.)

So thats a brief summary, and I want to reemphasize the fact that as
soon as we can we will get transcripts of all of the talks up on our
website. In addition, were asking all of the presenters to provide us
with their visual materials, and well try and put those up on our
homepage as well so everybody will have the opportunity to relook at the
slides.

CVM will review comments from the meeting in the public docket and
formulate the next steps on the threshold. Thats going to take us a
little time, as you might guess, to try and get through all the things
that were said and address comments. But please send in any additional
comments to the docket by April 9th, and well try and respond to those.

(Slide.)

Next steps, other next steps, categorization. Thats one of the other
areas in the framework document that we need to move forward on. CVM and
CDER, Center for Drug Evaluation and Research, the human counterpart of
CVM in the FDA are working on \-- has a working group already developed
to categorize antimicrobials as to the importance in human medicine, and
we will be working to develop a draft guidance document. The time frame
is some time in 2001. With all of the other things were doing sometimes
its hard to sit down and get some of these things done, but this is a
high priority. So we will try as hard as we can to make sure that we
dont allow any slippage in that.

Microbiological safety assessment, that is the preapproval studies that
are going to be needed, and we should have a guidance document on that
overall data to be addressed sometime in June of this year. So not too
very far away, and some of the kinds of studies were indicated in the
threshold document.

(Slide.)

Those are the summary and next step. Before we adjourn for the day I
just wanted to address one comment that I heard made yesterday, and I
just couldnt let it go. That was that some people have interpreted the
actions of FDA as using this as an attempt to quit approving
antimicrobial drugs in animals, or to withdraw all antimicrobial drugs
in food animals. I just want to say personally that that is the furthest
thing from the truth, that we are really trying to find a way and this
is our best approach at trying to find that balance between public
health and animal health.

I think Dr. Beaulieu in his opening statement two days ago took the very
courageous approach to say it is our responsibility. We do have
accountability to the welfare and the health of animals and that we will
try our very best to honor that responsibility and at the same time do
\-- honor our responsibility to maintain the competence in public health
as well. I would just say if it was our intention to stop approving or
withdraw antimicrobials we could have found a much easier way of doing
it than this, and cheaper as well. So I wanted to get that on the
record.

Lastly, I would like to just thank some of the people who have been
working tremendously hard to put this program on. So I would like to
read their names. Sharon Thompson, who was \-- took the original
responsibility for coordinating and getting all the speakers together,
as well as Bill Flynn. Those two really deserve a lot of thanks for
making this think happen. In addition to those people, I want to thank
some of the staff, people that youve already interacted with out at the
registration desk and making sure that all the refreshments were there
on time. Linda Cowatch, Heidi Burch, and Dave Lynch on the registration
desk. Joann Kla, who except for the first 10 minutes we had a flawless
technology, and that wasnt even \-- that was not Joanns fault at all,
but Joann and Kendra Gibbs for audiovisual assistance. Bob Miller, who
you havent seen here, but hes back at CVM paying the bills. He provide
financial and technical support to this. Aleta Sindelar for really
coordinating all of the administrative aspects of this meeting, the
logistics for the meeting, and making sure that everybodys
transportation is taken care of. If you had problems with your room or
anything else, that was Aletas job, and I think shes done a wonderful
job on that. Most of all, last but certainly not least, I want to thank
all of the people. All of the participants who spoke Ive already
mentioned, but all of the members of the audience who have paid
attention, have asked some very deep and probing questions, and who have
actively participated in this meeting. Thank you all very much, and I
wish you all a safe journey back home.

(Whereupon, the meeting was adjourned at 4:53 p.m.)