COMMITTEE MEMBERS
James D. AndersAGENCY REPRESENTATIVES
Janice Oliver, Deputy Director, Center for Food Safety and Applied Nutrition, FDA
Arthur P. Liang, MD, MPH, CDC Liaison
LeeAnne Jackson, FDA Liaison
Dr. Karen Hulebak, Executive Secretary
ALSO PRESENT
Dr. Paul Mead
Dr. Dale Hancock
Dr. Colin Gill
Dr. Isabel Walls
Dr. Charles Haas
Dr. Nancy Stockbine
Dr. Bonnie Rose
Dr. Mark Powell
Dr. Eric Ebel
Dr. Wayne Schlosser
Dr. Tanya Roberts
Ms. Peg Coleman
AGENDA ITEM
MS. OLIVER: Good morning. Once again, my name is
Janice Oliver, and I'm Deputy Director for FDA's Center for
Food Safety and Applied Nutrition.
I don't have to keep Bob Buchanan in place today.
I just have to keep Dane in place.
DR. BERNARD: Yes, ma'am. What is this, "pick on
Dane" day? Bruce is over here giving me all kinds of grief.
MS. OLIVER: I've got to pick on somebody, Dane.
I'll be chairing the meeting again today. Dr.
Kaye Wachsmuth is not able to be with us. However, Dr.
Hulebak is here this morning from FSIS and will be assisting
me in chairing the meeting.
This morning FSIS is going to be presenting risk
assessment models for E. coli 0157:H7. What they'll be
doing is there will be various presentations throughout the
day. The presentations are geared at about 45 minutes each,
allowing 15 minutes afterwards for questions. The
questions, as in the previous day, will be primarily for the
Committee and the invited experts to ask questions. If
there's still available time, then we'll ask those others
who are here at the meeting if there is time to ask
questions also.
First, the Committee is supplemented today by a
number of experts that FSIS has invited. I would like to
turn it over to Karen Hulebak to introduce those, and then
after that, I will ask the entire Committee and the experts
to introduce themselves again for the record.
DR. HULEBAK: Good morning, everybody. Thank you
all for being here to listen to this presentation by our
risk assessment team of our risk assessment for E. coli
0157:H7 in ground beef.
In order to assist the Committee and to add to its
expertise, especially in view of the fact that David Acheson
and Alison O'Brien can't be here today, we've invited a
number of experts to take part in this discussion, some of
whom have arrived and, I believe, some of whom have not.
Here with us presently are Dr. Isabel Walls, NFPA;
Dr. Colin Gill of Agriculture and Agrifood Canada; Dr. Paul
Mead of CDC; Dr. Nancy Stockbine of CDC, expected; Dr. Chuck
Haas of Drexel University; and Dr. Dale Hancock of
Washington State University.
I'd like next to introduce the risk assessment
team, the FSIS risk assessment team. They are seated at the
back of the room there. Most of these folks are from the
Food Safety and Inspection Service. The team is headed by
Dr. Mark Powell, and the members of the team include Dr.
Eric Ebel, Dr. Wayne Schlosser, Dr. Peg Coleman, and Dr.
Tanya Roberts, who's with USDA Economic Research Service.
We have a full day of presentation and discussion
for you. We'd like to take this day to make sure that you
hear from us in an appropriate level of detail, that is,
enough detail to inform you about our assumptions and the
model parameters and the outputs of the model with a degree
of detail that informs you and doesn't overwhelm you or,
worse yet, bore you.
There are several questions that we'd like you to
keep in mind as you listen to the presentation. With these
questions, we hope to focus your thinking about particular
aspects of the model, but please don't assume that this is
all we'd like you to--that this is all we seek your comment
on. We would like you to consider these particular
questions, but we welcome your comment on other aspects of
the model or other questions of the model that you might
have.
You have them before you. The question about
resolution we will leave for later. We may not get to it at
all today, but we would like you to consider the second
bullet there: Is there evidence that would allow us in this
model to adjust for the specificity of microbial analysis?
That's our major cross-cutting question.
With respect to the production section of the
model, can the Committee recommend a better way to link live
cattle to contaminated carcasses, the link that we try to
make in this model?
Are there data or methods currently available that
would improve the quantitative links among fecal, hide, and
carcass contamination? With respect to slaughter, what
evidence would be necessary to satisfactorily quantify the
link between hide and carcass contamination?
Second, with respect to slaughter, we've attempted
to develop a model, a mechanistic model that follows product
through the slaughter plant. Would it be preferable to
develop a strictly data-anchored model which does not
attempt to model processes between monitoring plants? If
that were possible, what data would be required to develop
such a model?
Regarding preparation of product, rather than
modeling beyond the last point where validation is currently
possible for raw ground beef, would it be preferable to
consider simply a proportional relationship between the
prevalence of 0157:H7 in raw ground beef and the incidence
of 0157:H7 illness due to consumption of ground beef?
Next, for preparation, how do we define a
plausible frequency distribution for extreme
time/temperature handling conditions in the absence of data?
And then, finally, for dose-response, are there
sufficient data and methods available to develop a separate
dose-response relationship for the susceptible sub-
population? How might we validate such a curve?
Is the basic envelope approach sound? And you
will hear more about that, of course, during the discussion
of dose-response.
Is it appropriate to anchor the most likely value
for the dose-response, the beta plus one envelope. The
envelope describes the various assumptions made about dose-
response covering the range of what we know.
Again, please think about these questions as the
ones that we would most like to hear from you on. Do not
limit your questions or your commentary to these particular
questions.
Also, while this is the one day we have to present
this full model to you, we hope you take the opportunity in
the coming couple of months to give us whatever suggestions
you have and ask us whatever questions you have. There's
some work we have yet to do on this model, and we have time
to incorporate any thoughts that you might have.
Any questions at this point?
[No response.]
DR. HULEBAK: All right. Then let's dive right
in.
MS. OLIVER: Let me just ask the Committee before
we go further to introduce yourself for the record since
several members are not here that were here earlier, and
I'll start to my right, please.
DR. WALLS: Isabel Walls with the National Food
Processors Association.
DR. GILL: Colin Gill of Agriculture Canada.
DR. RUSSELL: Leon Russell, Texas A&M University.
DR. JAHNCKE: Mike Jahncke, Virginia Tech.
DR. GROVES: Mike Groves, LSU.
DR. DICKSON: Jim Dickson, Iowa State University.
DR. SPERBER: Bill Sperber, Cargill.
DR. ROSE: Bonnie Rose, FSIS.
DR. SWAMINATHAN: Bala Swaminathan, CDC.
DR. MORALES: Roberta Morales, Research Triangle
Institute.
DR. ANDERS: Jim Anders, North Dakota Health
Department.
DR. LIANG: Art Liang, CDC.
MS. JACKSON: LeeAnne Jackson, FDA CFSAN.
DR. ENGELJOHN: Dan Engeljohn, USDA FSIS.
DR. DOYLE: Mike Doyle, University of Georgia.
DR. DOORES: Stephanie Doores, Penn State
University.
DR. ROBACH: Mike Robach, Conti Group Companies.
DR. KVENBERG: John Kvenberg, Food and Drug
Administration.
DR. NEILL: Peggy Neill, Brown University,
Providence.
MR. SEWARD: Skip Seward, McDonald's Corporation.
DR. LONG: Earl Long, CDC.
DR. TOMPKIN: Bruce Tompkin, ConAgra.
DR. BERNARD: Dane Bernard, NFPA.
DR. HANCOCK: Dale Hancock, Washington State
University.
MS. OLIVER: Okay. Thank you very much.
With that, Mark Powell will now give the
introduction and scope of today's meeting.
DR. POWELL: Thank you. Can everyone hear the
level fine? Bring it in closer? There, is that good?
Okay.
Well, thank you. On behalf of the USDA Food
Safety and Inspection Service E. coli 0157:H7 risk
assessment team, I'd like to thank the participating
agencies, members of the Committee, and the other invited
experts for providing us this opportunity to present the
draft FSIS risk assessment of E. coli 0157:H7 in ground
beef. The agency views your input as a key element of the
scientific peer review process that underpins informed food
safety decision-making.
Today we will be presenting the draft baseline
process risk model, that is, we will be presenting the model
of the as-is scenario that reflects the existing range of
practices and behaviors regarding the production, slaughter,
processing, preparation, and consumption of ground beef in
the U.S. The baseline model does not include any assessment
of the potential public health impacts of alternative risk
mitigation measures, and our purpose in presenting the draft
model is for scientific peer review, not for discussion of
the risk management options or the policy implications of
the draft model.
Next slide?
The full risk assessment team consists of members
in addition to today's presenters. The team has also
received significant contract support as well as input from
IFRAG, the Interagency Food Risk Assessment Group, which is
convened by the USDA Office of Risk Assessment and
Cost/Benefit Analysis, and we'd like to take this
opportunity to recognize their contributions. In the
interest of time, the presenters will refer to E. coli
0157:H7 simply as 0157.
Next?
I will lead off today's presentation with some
background and a definition of the scope of the assessment.
Eric Ebel will then summarize the outputs of the exposure
segments of the model, and Wayne Schlosser will present our
efforts to correlate the exposure segments of the model with
surveillance data. After a brief break, Eric Ebel will
present the production segment, and Tanya Roberts will
present the slaughter segment before lunch.
Wayne Schlosser will begin the afternoon session
with the preparation segment, followed by Peg Coleman with
the dose-response analysis. I will conclude the
presentations with a summary and then a comparison of the
model's predictions with an epidemiologic estimate of the
annual number of cases of 0157 due to ground beef. For most
of these segments, we have budgeted 45 minutes for the
presentation and 15 minutes for questions and discussion.
Next?
This slide places the assessment into context.
Since 1994 FSIS has treated raw ground beef with 0157 as
adulterated under the Federal Meat and Inspection Act unless
it is further processed in a manner that destroys the
pathogen. Most recently, several news sources of
information have begun to emerge suggesting that the
prevalence of 0157 is higher than previously reported.
Recently, FSIS issued a draft white paper on 0157 indicating
that the agency is considering its policy in light of this
emerging information. The production segment of the draft
risk assessment incorporates some of this new information
regarding herd and within-herd prevalence estimates. But
many of these studies have not yet been finalized or
reported in the scientific literature. Future iterations of
the model could incorporate new data as it becomes
available.
Next?
The 0157 risk assessment project began taking form
in March 1998 when I formed a resource group during the
formulation stage of the assessment. In October 1998, a
public meeting was held to solicit input at an early stage
of the process and to release a preliminary document
describing the overall modeling approach and summarizing the
data that had been acquired by the team to date.
Next?
We have received peer input during the development
phase of the assessment through presentations at the Society
for Risk Analysis, or SRA, and IAMFES, and by convening a
week-long interagency workshop on microbial pathogens in
food and water that involved microbial risk assessment
practitioners from USDA, FDA, EPA, the UK, and New Zealand.
The peer review process began earlier this week
with a presentation of the draft model at the 1999 SRA
meeting and continues today with the presentation before
this Committee.
Next?
Development of the E. coli 0157:H7 process risk
model, or ECOPRM, is intended to address multiple goals, and
at this point we have made the most progress towards
satisfying the first two goals of developing the baseline
model and comparing the predicted results to epidemiologic
estimates.
Next?
The scope and nature of the risk assessment is a
function of the questions that decision-makers could pose to
the analysis. If the only objectives of the assessment were
to estimate the magnitude of the problem of 0157 in ground
beef or, alternatively, to establish a risk-based standard
for ground beef products at the point of consumption, then
it would be sufficient to conduct an analysis of the
epidemiologic data or to analyze only the dose-response
relationship. The process risk model, however, is intended
to provide a broader decision-making tool; therefore, the
bulk of the model is the exposure assessment, which contains
the analysis of occurrence, growth, and decline of the
pathogen from farm to table. Our aim for the baseline model
is to be as consistent as possible with the observed data so
that we can use the model to identify potential critical
control points, evaluate public health impacts of
alternative mitigations, and identify key areas for
research.
Next?
The 0157 process risk model covers all aspects of
ground beef production and consumption from farm to table.
In the remainder of my presentation, I'll discuss the scope
of the assessment and the range of public health outcomes
associated with 0157 in ground beef. The exposure
assessment consists of three sequential segments. The
production segment outputs the prevalence of 0157 in live
cattle. The slaughter segment outputs the prevalence and
levels and 0157 in beef trimmings that are destined for
grinding. The preparation segment outputs the prevalence
and levels of 0157 in consumed ground beef servings. This
final output of the exposure assessment feeds directly into
the dose-response analysis, and then the final output of the
model is the annual number of 0157 cases due to ground beef
in the U.S.
Next?
The scope of the assessment is limited to ground
beef as a vehicle of infection and, therefore, does not
include cross-contamination to or from ground beef or a
person-to-person secondary transmission. The scope of the
present assessment is also limited to 0157 and, therefore,
does not include all entero-hemorrhagic E. coli. However,
the paucity of reported outbreaks due to non-0157 EHECs,
combined with the higher isolation rates of serotype 0157:H7
in prospective studies indicates that the other EHECs may
not attain the public health importance of 0157 in the U.S.
The scope of the assessment is also annual and
national. Although data are available at some points to
model seasonal or regional scale, insufficient data are
available to model slaughter, processing, preparation, and
other processes at seasonal or regional scales.
Next?
The scope of the draft assessment includes cooked
ground beef products. The present draft assessment does not
include products containing ground beef that are prepared by
means other than cooking, for example, fermented sausages.
We also have not included raw ground beef consumption, which
is a very uncommon practice in the U.S., but the ingested
doses would be analogous to very undercooked ground beef,
and this is considered.
Intact steaks and roasts are excluded because
potential surface contamination would very likely be
eliminated during cooking. The present draft assessment
does not cover other non-intact cuts of beef such as steaks
or roasts that have been blade tenderized or injected with
needles that may introduce surface contamination into the
interior muscle tissue. However, FSIS does plan to address
the other non-intact products in a subsequent iteration of
the risk assessment.
Next?
Infection with 0157 is associated with a variety
of public health outcomes ranging from asymptomatic carriage
to, in a minority of cases, death.
Next?
The primary risk assessment endpoint is the annual
number of cases of 0157 illness due to ground beef in the
U.S. This total can be disaggregated into cases of bloody
and non-bloody diarrhea; severe cases, defined as cases of
bloody diarrhea in which the patient seeks medical care;
hospitalizations; cases of hemolytic uremic syndrome or TTP,
HUS or TTP; and, finally, the annual number of deaths in the
U.S. due to 0157 in ground beef.
Next?
This table characterizes our uncertainty regarding
the magnitude of the 0157 problem from all sources and that
attributable to ground beef. I'll return later this
afternoon to the derivation of these figures from the
epidemiologic data, but our best estimate is that about 21
percent of all cases are due to ground beef. Note that
there is considerable uncertainty regarding this
epidemiologic estimate derived independently from the
process risk model. We will correlate this epidemiologic
estimate with the results of the baseline model.
Next?
Before concluding, I'll draw your attention to the
project's Website. We can provide that to you later so you
don't have to copy it down if there's insufficient time.
This is where we'll post the risk assessment report and
model and other project-related information to make it
electronically accessible. In addition, hard copies of the
report will be placed in the FSIS docket, and we invite all
interested and affected parties to submit comments on the
draft risk model and the relevant data to the FSIS docket.
Unless they're brief, I'd ask in the interest of
time, since we're running a little late, that we hold any
questions or comments regarding the scope of the assessment
until the discussion period that immediately precedes our
lunch break.
Now I have the pleasure of turning the podium over
to Eric Ebel to present the overview of the exposure
assessment outputs. Eric?
DR. EBEL: Thanks, Mark.
As we've progressed through this risk assessment
process, we've had occasion to present interim reports on
the model. Feedback from these presentations has suggested
the need for something up front that ties things together
and gives the audience a feeling for the big picture of the
model. Therefore, we want to begin our discussion of the
model with the end in mind.
In this segment, we'll present a general overview
of summary outputs from the model as well as how these
summary outputs correlate with observed data generated
outside the model. I'll be presenting the overview section
of this presentation, and Wayne Schlosser will present the
correlation section.
Risk assessments are generally broken down into
exposure assessments and dose-response assessments. In food
safety risk assessment, the exposure assessment models the
occurrence of doses of harmful pathogens in servings of a
commodity. For this overview, we'll concentrate on the
exposure assessment of the 0157 in ground beef model.
An important--sorry, go back to--I'm sorry. There
you go. Okay.
An important principle in resource management is
separation of variability from uncertainty. We'll discuss
this principle before presenting our results. As we present
summary outputs of the model, we will describe the
variability in these outputs and the associated uncertainty.
We'll consider outputs from the production, slaughter, and
preparation segments of the model as all part of the
exposure assessment. We won't go into any detail as to how
these distributions were derived at this time. Each of the
model segments will be discussed in excruciating detail
later today.
Variability describes naturally occurring
differences that we note within populations or between
populations. Variability also results from sampling
something less than the whole population.
In the model, frequency distributions represent
variability in the system. For a given scenario of the
model, we consider these frequency distributions fixed. For
example, within-herd prevalence varies from one infected
herd to another. A frequency distribution describes the
proportion of affected herds at any given time that have,
let's say, 1 percent or 10 percent within-herd prevalence.
The number of organisms per square centimeter on a
contaminated carcass also varies from carcass to carcass.
But a frequency distribution describes what proportion of
contaminated carcasses have an average of, say, 0.1 CFUs per
cm2 or 1 CFU per cm2.
The temperature that ground beef is exposed to
when handled out of compliance with the model food code
varies from instance to instance of noncompliance. The
frequency distribution of the population of noncompliant
handling episodes descries this variability across the
population.
DR. POWELL: I just wanted to make the Committee
aware that there aren't handouts if you're looking to track
this presentation. We have handouts just for the segments
that will be production, slaughter, preparation, dose-
response. Just for clarification.
MS. OLIVER: Mark, can I ask you to introduce
yourself? And I'd just remind everybody that for the
recording and for the transcription, if everybody could just
reintroduced yourself for the record each time you speak.
DR. POWELL: I apologize. This is Mark Powell of
FSIS. And I'll turn the podium back over now to Eric Ebel.
DR. EBEL: In contrast to variability, which is
simply a reflection of nature, the concept of uncertainty
refers to our confidence in the true value or true frequency
distribution of something. Probability in most of our model
refers to a measure of confidence. Probability is
equivalent to the likelihood of something occurring or being
correct. So if we know that variability in the model is
represented by a frequency distribution and we are not
completely certain of which frequency distribution is the
true or correct distribution, we model several different
distributions to account for our uncertainty.
Examples of uncertainty in the model include the
prevalence of fecal-shedding cattle at slaughter in a given
year. There is some fixed prevalence, but we are uncertain
of the true fraction. We also know that CFUs per cm2 on
contaminated carcasses can be described by a frequency
distribution, but we are uncertain as to the true frequency
distribution. Similarly, the frequency distribution
regarding product temperature when out of compliance is
uncertain.
As we propagate uncertainty through the different
stages of the model, we must consider whether our
uncertainty is independent or dependent. Uncertainty
describes the likelihood that something is correct. If we
are incorrect at the high end of one input, are we more or
less likely to be incorrect at the high end of another
input? If the answer is no, then the uncertainties in model
inputs are independent. Otherwise, they are dependent to
some degree.
One technique for modeling independence and
uncertainty is called second-order modeling. Basically this
involves taking random samples from all uncertainty
distributions and evaluating the results conditioned on
these random draws. Another technique for handling
uncertainty is called boundary analysis. Underlying this
approach is the assumption that uncertainty may or may not
be correlated. We have chosen this approach for describing
uncertainty in the model for this presentation.
Therefore, we've defined three scenarios to
propagate through the model: a lower bounds, a most likely,
and an upper bounds scenario. The most likely scenario uses
averages for uncertain inputs. When considering frequency
distributions, we selected the central distribution from the
family or curves available. The lower and upper bounds use
10th and 90th percentile values for all uncertain inputs, or
the extreme frequency distributions for those cases where a
family of curves is available. These boundary scenarios
clearly represent a case where our uncertainty is positively
and completely correlated, but the interval between the
boundaries includes every other possible correlation,
including the assumption there is no correlation in our
uncertainty.
We modeled ground beef production and consumption
from the farm to table. We're dealing with a product that
originates from different classes of animals and changes
form as it moves from farm to table. Furthermore, the
environmental conditions that the products and the 0157
organisms contained within them are exposed to depend on the
transportation, storage, and handling of the products.
We modeled two general types of cattle operations.
Breeding operations are relatively small. About 20 percent
of all cattle slaughtered in the U.S. are culled breeding
cattle. On average, we assume that cattle culled from these
operations are slaughtered independent of one another.
Feeding operations tend to be larger operations.
About 80 percent of the cattle slaughtered in the U.S. are
feeding-type cattle. Cattle from these operations are more
likely to be shipped to slaughter with others from the same
operation and cannot be considered to be slaughtered
independent of one another. Cattle in these feedlots are
usually shipped in lots of 40-head capacities. We use the
40-head truckload as a basic unit for comparing live cull
and feeder cattle at slaughter.
This is a model output from the production segment
of a risk assessment. It is a frequency distribution for
the number of culled breeding cattle that are shedding 0157
in their feces. As this graphic shows, the number of
shedding culled cattle within a 40-head sample varies. This
frequency distribution is the most likely scenario result.
This graph overlays the upper and lower bounds
scenarios with the most likely scenario distribution from
the previous slide. As these distributions show, the lower
bound predicts there are higher frequencies of smaller
numbers of infected cattle per 40-head truckload.
This graph shows the same results for feeding
cattle. Again, this graph overlays the lower and upper
bounds scenarios on the most likely distribution. It is
clear from this analysis that there is less uncertainty
associated with feeding cattle than breeding cattle.
The slaughter segment of the model comprises two
basic types of slaughter plants. We model one plant type
that slaughters feeding cattle. Ground beef is a by-product
of this model plant type. We also model a plant type that
slaughters culled breeding cattle. Ground beef is a primary
product of this model plant type.
Overall, about two-thirds of all ground beef in
the U.S. is generated from feeding cattle, while the other
one-third is generated from culled breeding cattle. For
each slaughter plant type model, two forms of meat trimmings
are aggregated. Combos are modeled as 2,000-pound
aggregates of meat trimmings, while boxes are modeled as 60-
pound aggregates.
This chart shows the log of CFUs in contaminated
combo bins generated from fed cattle. As you can see, when
combo bins are contaminated, they are usually contaminated
with relatively low numbers of 0157 bacteria. Note that
these represent total organisms in a combo. The
concentrations per gram of contaminated combo bin would be
quite low since these bins contain about 1 million grams.
Here's the same graph with the upper and lower
bounds overlaid. This graph also shows the log CFUs in
contaminated combo bins, but these combo bins are generated
from culled breeding cattle.
This is the same graph then with the upper and
lower bounds overlaid.
Combo bins and boxes of meat trimmings are
composed of different ratios of lean to fat. During the
mixing and grinding of trim, different numbers of combo bins
and/or boxes are combined to generate grinder loads of
ground beef. The mixing and grinding of trimmings occurs in
large commercial operations or smaller retail settings, and
there's a wide variability in how trimmings are combined.
Overall, about 92 percent of ground beef is
generated from grinding combo bins of trim. The other 8
percent is generated from grinding boxes of trim or retail
trim. Many products are generated from the grinding of meat
trimmings. These varied products are also handled in many
different ways during distribution and preparation.
The output from the preparation model is an
exposure distribution. The most likely exposure curve is
shown here. In this graph, the x axis is in log CFUs per
contaminated serving, while the y axis is in log number of
servings. The shape of the curve suggests that contaminated
servings are most frequently contaminated with small numbers
of organisms.
This is the same exposure distribution with the
upper and lower bounds overlaid. These boundaries suggest a
great deal of uncertainty regarding the true exposure
distribution.
This is our last slide in this overview
presentation. It summarizes average model output across the
three exposure segments. It's a bit busy, so let me explain
it.
All of the numbers here are averages. We've
weighted breeding and feeding output by the production of
cattle and product generated by each of the types.
Furthermore, the concentration data is represented in all
cases on a per-gram basis. Finally, these results reflect
the most likely scenario for the model's outputs.
The bars show the prevalence at each stage.
Starting at the left, we see that an average of 11 percent
of all live cattle enter slaughter plants shedding 0157 in
their feces to some degree. The average prevalence of
contaminated carcasses just after dehiding is 4 percent. As
we aggregate trim from carcasses into combo bins, we see the
prevalence of combo bins with at least one CFU of 0157 in
them is 23 percent. As we aggregate combo bins into grinder
loads, the average prevalence of contaminated grinders
generated from combo bins is 81 percent.
Finally, after preparation and cooking of ground
beef meals, the model predicts that about 2 in every 100,000
servings contain one or more 0157 organisms. The line in
this graph shows the average log CFUs per gram of
contaminated material. Although we don't explicitly model
the number of 0157 organisms per gram of feces, we use an
average of 2.5 logs from published data here.
On carcasses, the model predicts an average of
negative 1.5 logs per gram of trim generated from
contaminated carcasses.
As trim from multiple cattle are aggregated into
combo bins, the average concentration per gram of combo bin
decreases to minus 4.5 logs. Because there is some
possibility for multiplication of 0157 within combo bins,
the concentration increases slightly in grinder loads.
Finally, because the average serving size is
around 100 grams, the concentration per gram of contaminated
serving increases to about minus 1 logs, or about 10 0157
organisms per contaminated serving.
Now, this finishes our overview of the model.
We'll proceed now directly then to the correlation of model
outputs.
DR. SCHLOSSER: I'm Wayne Schlosser from FSIS.
Models should reflect the state of the world to
the extent data is available to describe it. Consequently,
we attempt to correlate the model output with the state of
the world by either anchoring the model to real data within
the model or by validating the model with data external to
the model. This correlation offers assurance that the model
does reflect the state of the world to the extent possible.
Where possible, we've considered the implications
of surveillance data within the structure of our model
inputs. In some cases, we needed to develop intermediary
empiric models to analyze the surveillance data. These
empiric models then apply particular inputs for the final
model. Comparison of the model output to real-world data is
known as validation. In general, data used to validate the
model is not included during construction of the model.
This data thus provides an independent benchmark for
comparison. In some cases, independent data is not
available for validation.
Data for correlation purposes needs to be
representative. Fortunately, FSIS has analyzed samples for
0157 from a cross-section of the slaughter and processing
industries. For example, year-long baseline studies of
carcass contamination were conducted prior to implementing
HACCP.
FSIS also routinely collects ground beef samples
for 0157 analysis. Recently, a study in Canada was
published which surveyed cattle status at the slaughter
plant. We compared the implications of these three sources
of data with our model outputs for the exposure segment of
the model. And, of course, human case number estimates are
also available for comparison with our model's predictions.
Mark Powell will discuss those comparisons later today.
Whatever the surveillance data might be, it
usually needs to be adjusted to account for uncertainty.
Point estimates of percent positive will not suffice in
describing our confidence in the results. In some cases, we
need to account for the sensitivity of methods used. We
must also recognize the effect of sample size, both number
of samples and the quantity of sample collected in these
surveillance data. Therefore, surveillance data is
represented in our analysis with attendant uncertainty.
As we mentioned previously, our model output
uncertainty is represented by lower and upper bounds. For
comparison with surveillance data, we represent modeled
output as confidence bars extending from the lower to upper
bound, with the most likely output indicated between these
extremes.
The first point in the model where data exists for
comparison is the frequency of live cattle that are fecal
shedders at the slaughter plant. This Van Donkersgoed study
was conducted in a Canadian slaughter plant during a one-
year period. Since we did not use this data in developing
our estimates for the production segment of the model, this
comparison can be considered strictly as validation.
Overall, the study found 12 percent of steers and
heifers were 0157 positive at slaughter, while 2 percent of
culled cows were positive. The study used very sensitive
fecal sampling and culturing methods, so a little adjustment
for sensitivity was needed to compare these results with the
output of the production segment of the model.
This graph compares the uncertainty distribution
for the Canadian study's culled cows to the model's output
for cows and bulls just before slaughter. The red line
represents the range between the upper and lower bounds of
the model, with the green diamond representing the most
likely value. The blue line is the likelihood distribution
for prevalence derived from the Canadian data.
While there is some overlap between this
surveillance data in the modeled output, the model is
predicting slightly greater prevalence relative to the
Canadian study. In this graphic, the Canadian data has been
adjusted for test sensitivity, and the relative likelihood
of prevalence has been calculated using the binomial
distribution.
This graphic shows how the Canadian data match up
with the modeled output for steers and heifers just before
slaughter. In this case, the data and the model clearly
overlap.
Moving on, we considered the FSIS baseline
sampling data collected prior to HACCP implementation.
Samples representing three separate areas of approximately
300 square centimeters were collected from carcasses of cow
and bulls and steers and heifers. In the steer and heifer
baseline study, approximately 0.2 percent of carcasses were
positive for 0157. Cow and bull carcasses yielded no
positive results.
Enumeration of the positive samples revealed that
the most probable number of organisms on the positive
sampled areas ranged from 0.03 CFU per cm2 to 3 CFUs per
cm2.
This sampling data was used to construct a simple
empiric intermediary model. In this model, we assumed the
amount of carcass surface area contaminated could range from
300 square centimeters, the areas sampled in the baseline
study, to about 30,000 square centimeters, or the entire
surface area of the carcass.
If we assume the entire surface area of the
carcass is contaminated, then we would expect that FSIS
sampling methods, given the number of bacteria present,
would identify 77 percent of all contaminated carcasses. On
the other hand, if only 300 square centimeters were
contaminated, the sensitivity of the sampling procedure
drops to about 25 percent. These bounds on sensitivity thus
allow us to predict the prevalence of positive carcasses to
be from about 0.25 percent to 0.75 percent.
We next constructed simulated combo bins, each
holding trim from 75 cattle. The resultant frequency
distribution for contamination in combo bins allowed us to
predict the frequency and extent of contamination in grinder
loads. The model then simulated ground beef sampling and
testing in accordance with the FSIS procedures.
When we tested our upper bound assumption that the
entire surface area of the carcass was positive, the model
predicted that about 0.14 percent of 25-gram samples would
be positive and about 1.4 percent of 325-gram samples would
be positive. FSIS ground beef sampling data for 1995
through 1997, however, yielded only 0.08 percent positive
25-gram ground beef samples. In 1998, with a larger sample
size of 325 grams, FSIS found 0.33 percent of ground beef
samples positive, still well below the upper bound predicted
by the model.
The lower bound assumption of 300 square
centimeters of contaminated area significantly
underpredicted the number of positive samples that would be
found. Thus, a value for contaminated surface area
somewhere between these extremes seemed likely.
When we assume a contaminated surface area of
3,000 square centimeters, which is the log midpoint between
assuming the entire surface area is contaminated, and
assuming only 300 square centimeters are contaminated, the
predicted number of positive ground beef sample is
consistent with both the 25-gram and 325-gram sample size
results reported by FSIS. Thus, we anchor the contaminated
surface area in our full slaughter model at 3,000 square
centimeters. So let's look at the output generated from
that slaughter model.
This chart compares the prevalence of positive
carcasses from cows and bulls predicted by the model with
FSIS sampling data. The dark blue line represents the
likelihood of different prevalence levels, given the
sampling data. The model tends to slightly overpredict the
number of positive carcasses when compared to the sampling
data. Please note that the range of uncertainty from the
model is due to the cumulative effect of all the uncertain
inputs that contribute to this output as well as the method
we are using to communicate our uncertainty.
This chart is similar to the previous one, except
steers and heifers are compared, and as in the previous
chart, the model tends to slightly overpredict compared with
FSIS sampling data.
As you saw earlier, we used FSIS ground beef
sampling data in constructing our intermediary models. From
1995 through 1997, FSIS used a sample size of 25 grams to
represent a grinder load and found four positive samples out
of 4,999 collected. In 1998, FSIS began using a sample size
of 325 grams and found 12 positive samples out of 3,597
collected.
This chart shows the overlap of ground beef
sampling predicted by the model with the actual likelihoods
calculated from FSIS testing of 25-gram samples.
This chart shows the same overlap for 325-gram
samples.
In conclusion, the model is anchored in observed
data as we look at live cattle, carcasses in the slaughter
plant, and samples of ground beef leaving the grinder.
Unfortunately, there is no data available that directly
measures the number of humans that are actually exposed to
0157 from ground beef. Also, as we noted, the model output
boundaries tended to be wider than the confidence limits of
the data. This is to be expected considering all of the
uncertain inputs to our model and the type of uncertainty
analysis performed. This analysis propagates increasing
uncertainty as we progress from farm to table.
We'd be glad to answer any questions you might
have regarding both the correlation analysis and the
overview.
DR. GILL: Colin Gill, Agriculture Canada. Aren't
the observed data numbers so small that you can't really
make any correlation at all between your model and the
observed numbers? I mean, if somebody licked their finger,
you would get--it would throw your correlations right out.
DR. SCHLOSSER: Well, we didn't think so.
DR. EBEL: Which data are you talking about?
DR. GILL: The number of positive samples in the
observed data are so small that I can't see how you can
correlate anything with your model.
DR. EBEL: Is that concerning carcasses or ground
beef or--
DR. GILL: The whole lot.
DR. EBEL: --or all of it?
DR. GILL: All of it.
DR. EBEL: Well, the data is what the data is.
Certainly, as--data increases your confidence in what the
data is saying is narrowing down and certainly suggesting
higher likelihood in the implied prevalence or
concentration, or whatever it is we're measuring, but it
certainly reflects what those results were and the
distributions in terms of the uncertainty are reflected, I
mean, as objectively as we can reflect them. To add
increased uncertainty beyond what the data implies doesn't
seem warranted in this case.
MS. OLIVER: Dane?
DR. BERNARD: Thank you. Dane Bernard.
Your consideration was fed cattle, steers and
heifers, and culled breeders, and I'm not a professional in
the beef industry by any measure, but I expected some culled
dairy animals possibly to be included. Is this not a
significant portion of meat that goes into ground beef comes
from culled dairy animals, or am I mistaken there?
DR. SCHLOSSER: We've included both culled dairy
and culled beef animals in the breeders.
DR. BERNARD: Okay. And another question. The
Canadian study that you referred to, that study also
included culled animals? Was it targeted to culled animals?
Because I noticed you compared the outputs from the Canadian
study with the calculations that you'd made on culled
animals in the States.
DR. EBEL: They actually stratified their results
based on culled breeding cattle and feeding cattle. So we
actually have those results summarized. I don't know if we
can page up to that. Maybe we can.
Those results there at the bottom of that slide
are the reported results from the Canadian study, so 12 out
of 593 culled cattle were sampled and found positive.
DR. BERNARD: Not to roll back the clock two days,
but I just wanted to make sure we're comparing apples to
apples here.
In addition, I'm assuming that the cattle that
would have been in the Canadian study would have come from a
climate somewhat northern than most of the cattle that would
be in the U.S. study. I have seen papers that seemed to
relate geographic areas with prevalence of certain pathogens
and related to climate. Is there an effect there that
should be compensated for or considered? I notice we had,
you know, some uncomfortably large uncertainty bars there,
and, again, I'm not a professional with that, but I'm
wondering how much might be due to factors that may not have
been compensated for.
Thanks.
MS. OLIVER: Jim? And if I could ask the
presenters when you're speaking, since you have two of you,
to identify yourself before giving responses. Thank you.
DR. DICKSON: Jim Dickson, Iowa State University.
I think it's a general question. Is this information, are
these graphs available on your Web page? Because I had some
specific questions on the data which I'd really--I'd like to
have the graphs in front of me rather than trying to watch
them on the screen as they go by. Is there an opportunity
to see all this on your Web page or where would we get
copies of this?
DR. POWELL: When the draft report is produced,
we'll place that on the Web page, and a copy will be
submitted to the docket. This is Mark Powell responding.
DR. DICKSON: But as we sit here today, there's
not an opportunity to get a hard copy of this, then?
DR. EBEL: Do we have a hard copy of this
presentation available?
DR. POWELL: Do we have the capability to do that,
Karen?
DR. HULEBAK: I think you do, yes.
DR. POWELL: Yes, we'll have the disk taken over
and get hard copies made.
DR. DICKSON: It doesn't necessarily have to be
today, but if we could get copies of it for--
DR. HULEBAK: Okay, sure. Any one of you who
wants more information, which is absolutely available, about
any section of this discussion today, let us know and we'll
send it to you forthwith.
DR. DICKSON: Okay. Thank you very much.
DR. HULEBAK: I'd also like to acknowledge the
recent arrival of Dr. Chuck Haas, Drexel University, and Dr.
John Kobayashi.
MS. OLIVER: Thank you. Mike Doyle?
DR. DOYLE: Mike Doyle, University of Georgia.
I'm a bit unclear as to where you come up with some of these
numbers. For example, you've got 11 percent of the cattle
shedding E. coli 0157. What's the basis for that?
DR. EBEL: Well, I guess, as we started off, we
are going to be presenting each of the segments of the model
in sequence, but we wanted to give sort of the results up
front, and we hope that some of these distributions will
become clearer as the day goes on in terms of how they were
derived.
DR. DOYLE: All right. I'll wait. Thank you.
MS. OLIVER: Skip?
MR. SEWARD: Skip Seward, McDonald's. If I read
your one slide correctly on the combos when you were
predicting contamination levels, then your comparison on
that was to data which you had for ground beef. Is that
because--if I saw that correctly, is that because you just
didn't have data on combo contamination and that's why you
used that as a comparison?
DR. EBEL: Yes, we don't have any data available
that represents a good cross-section of combo bins. The
comparison was actually at the grinder load levels, which,
you know, represents an aggregate, and each grinder
represents two or more combo bins that have been combined to
be ground. So the actual comparison is at the grinder load
level.
MR. SEWARD: So your levels would be higher there
based on what you showed earlier.
DR. EBEL: Right. The prevalence of contaminated
grinder loads is higher than the prevalence of contaminated
combo bins coming out of our model. But the actual sample--
the comparison is really at a sample level, a sample taken
from a grinder load, what's the likelihood of it being
positive, which incorporates both the prevalence of
contaminated grinder loads, but also how many organisms are
in there that are even available to be detected. So as you
see from this, the raw prevalence data suggests very low
frequencies of draws would be contaminated based on just
going out and randomly sampling contaminated--or across the
population of grinder loads.
MR. SEWARD: Thank you.
MS. OLIVER: Mike Jahncke?
DR. JAHNCKE: Mike Jahncke, Virginia Tech.
Getting back to a comment that Dane made, is there
any attempt here to split out your culled dairy from your
culled cattle, or are they lumped together?
MS. OLIVER: Can you please identify yourself
again for the record?
DR. EBEL: Eric Ebel. We'll go into that in the
production segment, but just to say that we did not separate
dairy from beef cow/calf cull animals. We considered them
combined because that's typically how they're managed at the
slaughter plant level, and statistics are available at that
level of aggregation. So we haven't separated dairy from
beef industry cull animal, but consider them together.
DR. JAHNCKE: Is there a possibility at some point
that you can split those out, or is it just a function of
insufficient data to be able to split them out?
DR. EBEL: I guess that's a justification at this
point. We don't have very much evidence on the beef
cow/calf side. As we get into the production segment,
hopefully some of that evidence will come out.
DR. JAHNCKE: Thank you.
MS. OLIVER: Swami?
DR. SWAMINATHAN: Bala Swaminathan, CDC. I just
needed a clarification.
On the comparison of the Canadian surveillance
data with the model output, the model prediction was higher
than what the surveillance data indicated, and you made a
statement--also the Canadian study apparently used a more
sensitive method. You made a statement that the Canadian
data were adjusted for test sensitivity. Could you clarify
that, please?
DR. EBEL: We adjusted for the sensitivity, but we
did want to point out that the methods that were used up
there represent more sensitive methods than have been used
maybe--I don't want to say "traditionally"--I'm sorry,
again, Eric Ebel--have been used traditionally, but we still
needed to adjust that data because what we're trying to
represent in the model would be what we would call a true
prevalence of, you know, cattle that are shedding 0157
organisms, and the data that is presented on the Canadian
research still is going to have some false negative results
in there. So we wanted to adjust for that, and I believe we
used a sensitivity of 96 percent, so that 96 out of every
100 infected cattle would actually be detected in the
Canadian study based on that assumed test sensitivity. But
we still needed to make that adjustment as we made the
comparison.
DR. ROSE: Bonnie Rose, FSIS.
Wayne, for the 1992 to '94 steer heifer and
cow/bull baseline studies, did you indicate that the sample
size was 300 square centimeters, or did I hear that
correctly?
DR. SCHLOSSER: Three separate areas of 300 square
centimeters.
DR. ROSE: I believe the total area sampled was 60
square centimeters by the excision method, 20 square
centimeters at each site.
DR. SCHLOSSER: Okay.
DR. ROSE: The actual analytical unit was 60
square centimeters.
DR. EBEL: Yeah, sample area sampled.
MS. OLIVER: Mike?
DR. ROBACH: Mike Robach, Conti Group.
My question also relates to the sampling, and I
was wondering when you were considering the 300 centimeters
versus the whole carcass assumptions, were you looking at
the 300 centimeters randomly or was this a site-directed
sample?
DR. EBEL: Eric Ebel. We assumed basically a
random sample in our analytic approach. We didn't use a
targeted approach, obviously. In the baseline study there
were targeted areas that were actually samples, so this was
a simplification in our analysis that we made.
DR. ROBACH: Well, just so I understand, so the
300 square centimeter sample in the model would have been a
random 300-centimeter site; is that correct?
DR. EBEL: Right, times three.
DR. ROBACH: Times three.
MS. OLIVER: Are there any other questions? Leon.
DR. RUSSELL: Leon Russell, Texas A&M University.
You mentioned early prevalence of fecal shedders
per year. How as that data collected? Was that purely
prevalence or was that cumulative incidents?
DR. EBEL: I believe the context--this is Eric
Ebel--the context that was in the example of uncertainty,
that if we could know what the prevalence of fecal shedders
across all slaughter plants in the US that are killed in a
given year, you know, we would obviously need to sample with
100 percent accuracy, but whatever estimate we get we're
going to have some uncertainty about that number. We
haven't measured--and as far as I know, nobody has measured
prevalence of fecal shedding in cattle across all slaughter
plants in the US, but at some point, when that data becomes
available, there will be attendant uncertainty simply
because we can't sample all the cattle, and as a
consequence, any estimate has some measurement error in it.
DR. RUSSELL: Thank you.
MS. OLIVER: Peggy.
DR. NEILL: Peggy Neill.
I'm not sure if I should direct the question to
you all or to the Committee at large. Is cattle slaughter
equally distributed or equally frequently conducted across
all months of the year? Because I think you can see where
I'm going, is that if cattle slaughter does not occur
equally across months of the year, and frequency of fecal
shedding is not equal across months of the year, then the
model might have to take that into account.
DR. EBEL: Well, we'd be glad for somebody else to
comment. As far as we know, we believe that in general
there's a uniformity. Certainly within the culled breeding
cattle part of it, there's some seasonality or cycles that
go on as a result of seasonal patterns in breeding and so
forth, but in general, we would believe that the cattle
slaughtered on a monthly basis are relatively constant, as
far as we can remember.
MS. OLIVER: Dan, do you have a response to that?
DR. ENGELJOHN: Dan Engeljohn, USDA.
Yes, I would say that it is reasonably uniform
throughout the year, and we certainly have access to that
information, so we can come up with it.
Can I follow up with a question?
MS. OLIVER: Sure.
DR. ENGELJOHN: I think this is directed towards
Wayne.
You had made a statement that with regard to the
combo bins, that contained 75 cattle or the product of 75
cattle. I'm just curious. Is that something that you knew
or is that an assumption that you had made?
DR. SCHLOSSER: That was an assumption just for
that basic model that we used, trying to correlate as we
were going along. As we go into the slaughter model you'll
see that we have a range to that, varying from just a few
cattle if it's cows and bulls, to perhaps more cattle than
that if it's steers and heifers.
DR. ENGELJOHN: This is Dan Engeljohn, the follow
up.
Just to let you know, we do have information, or
we have received information about what is expected to be in
a combo bin in terms of what that represents, so I'm curious
to hear what you have to say later.
DR. POWELL: This is Mark Powell. And again, I'd
just like to follow up that the intermediate empiric model
that Wayne presented was simply designed to try and get our
best estimate for the surface area that would be
contaminated on a carcass between the bounding estimates.
Okay. So it is an input into the slaughter model, and the
model that Wayne elaborated was simply designed to identify
the most likely within those bounds as an input, not as an
output to the model.
MS. OLIVER: Thank you. With that, we'll take a
15-minute break and come back at about 9:35. Thank you.
[Break from 9:16 a.m. to 9:38 a.m.]
MS. OLIVER: The first thing I would like to
announce is that we didn't say anything about public comment
before, and the agenda didn't have anything, but if somebody
wants to sign up for public comment, we'll have an
opportunity for that later. You can sign up at the desk
here or at the table outside, and we'll allow that. We'll
see how many people sign up and find an opportunity for
that.
Our next presenter is --
DR. EBEL: Eric Ebel.
MS. OLIVER: Right, Eric Ebel on production and
Karen Hulebak is going to introduce the section on the
questions that FSIS wants answered.
DR. HULEBAK: Thanks, Janice.
Just to reiterate, as you listen to this section
on production, keep in mind the following two questions.
Can you, the Committee, recommend a better way to link live
cattle to contaminated carcasses? And second: Are there
data or methods currently available that would improve the
quantitative links among fecal, hide and carcass
contamination?
DR. POWELL: This is Mark Powell.
I have a housekeeping comment, and that is that
there have been some typos and other modifications made to
the handouts that were sent out to you earlier. We
apologize for that, but we will get a final set to you of
what is presented on the screen. I don't think there should
be any problem following the rest--the remainder of today's
presentations, and we will be submitting the final copy to
the docket and distributing it to the Committee and the
invited experts.
MS. OLIVER: Thank you.
I'd just like to make one more announcement, and
that is that Paul Mead and Nancy Strockbine from CDC have
now joined us as the invited experts, so welcome.
And now we'll continue on with Eric Ebel. Excuse
me. Jim Dickson had a question. I'm sorry.
DR. DICKSON: This is just a general comment. Jim
Dickson at Iowa State.
I don't know how the rest of the Committee feels,
but I would be more than happy to take these in electronic
format, as opposed to getting another stack of handouts to
take home or carry with me in the mail.
MS. OLIVER: Sure.
DR. POWELL: Thank you. And we can accommodate
that. We have it electronically available in PowerPoint
format. For those of you that operate in another
environment, I apologize. We would be more than glad to
send the data electronically. It's much easier on us. So,
yes, we'll do that, we'll send them electronically, and if
you could just let the Secretariat know if that's going to
be inconvenient for you, and we can make arrangements to
have them sent via hard copy.
MS. OLIVER: Right. I think that's best. We'll
send--our default will be to send electronically. If you
want hard copy, please let me know. Okay.
DR. EBEL: Thank you.
Undoubtedly 0157 contaminated or infected cattle
entering the slaughter process influence the contamination
of ground beef. Yet, our understanding of a quantitative
association between incoming status of slaughter cattle and
outgoing status of meat harvested from the cattle is
limited.
At this point the quantitative link between pre-
harvest and post-harvest contamination is only established
for those cattle that are fecal shedders of 0157, and that
link is tentative. Consequently, we will limit our modeling
of live cattle status to fecal shedding. We expect that
data linking hide contamination to carcass contamination is
forthcoming, however.
The production segment is the first part of a
farm-to-table model. Its purpose is to simulate the
proportion of live cattle at slaughter that are 0157
infected.
There's a lot of data pertaining to the occurrence
of 0157 in live cattle. Therefore, our challenge in this
segment is to coalesce this sometimes conflicting evidence
into a cohesive picture of what we think the true occurrence
is. I will present information on the development of the
production model and the data used to estimate its
variables. I will also present some provisional results, as
well as discuss data gaps for this model that could be
filled through additional research.
The 0157 Process Risk Model--doesn't want to stay
up, does it--they say the process risk model begins where
the production of beef begins, at the farm. Most of the
information available on the occurrence and distribution of
this organism in US livestock has been collected during
surveys of farms and feedlots.
Many risk factors hypothesized to influence 0157
status in cattle are factors that apply to whole herds. Am
important reason for incorporating the farm in the process
risk model is that reductions in the prevalence of affected
cattle entering slaughter plants will be accomplished
through actions on the farm of feedlot.
The production segment separated culled breeding
cattle from feeding cattle. We do this because the
slaughter, processing and distribution of meat from these
types of cattle is different. Feeding cattle are defined as
cattle sent to slaughter from feedlots. Typically, these
cattle are steers and heifers. Steers and heifers comprise
about 80 percent of all cattle slaughtered in the US
annually. Culled breeding cattle are defined as cattle sent
to slaughter from dairy or beef cow calf herds. These
cattle are typically mature cows or bulls. Cows and bulls
comprise about 20 percent of all cattle slaughtered in the
US annually.
The three general states of the production segment
are: on-farm, transportation and slaughter plant. The on-
farm stage estimates the within-herd prevalence of 0157
infected cattle and the herd prevalence of 0157 affected
herds. In the transportation stage we considered the effect
of transit time and commingling on the transmission and
amplification of 0157 infections. Yet, all the
observational evidence suggests that there is no substantial
difference in fecal prevalence between the farm or feedlot
and the slaughter plant. Therefore, we model no change in
prevalence between the farm or feedlot and the slaughter
plant. In the slaughter plant stage we consider the effect
of cattle clustering as they enter the slaughter plant.
Whether they originate from feedlots or breeding
herds, cattle destined for slaughter must be shipped to a
slaughter plant. During shipment transmission of 0157 may
theoretically occur. Alternatively, some infected cattle
may clear their infection during shipment. The available
evidence shown here does not imply there are dramatic
differences in fecal prevalence between the farm and
slaughter plant. Transit between the farm and slaughter
plant may not affect prevalence of infected cattle, but it
may be important in causing changes in hide prevalence.
Studies of hide contamination with Salmonella suggests an
increase in prevalence of hide contaminated cattle between
the farm and slaughter. Unfortunately, there is no data on
0157 hide contaminated cattle at the farm, and only limited
data concerning hide prevalence at the slaughter plant.
Therefore, inclusion of the effect of transit time on hide
contamination in this model awaits the availability of such
data.
Culled dairy and beef cows and bulls arrive at the
slaughter plant from their farms of origin after transit on
trucks. The majority of these cattle arrive after first
being shipped to one or more livestock markets, where they
are auctioned to the highest bidder, then shipped to
slaughter. The combined average herd size for beef and
dairy herds is approximately 300 cows. According to survey
statistics, approximately 25 percent of cows in dairy herds
and 11 percent in beef hers are culled each year. These
culling percentages imply that the average herd would market
about 1 to 1-1/2 cattle per week. Given the low number of
cattle contributed per herd and the commingling of cattle in
livestock markets, it is reasonable to assume random mixing
of culled breeding cattle at slaughter plants. Such an
assumption implies that the probability of infection is
independent between cows at slaughter.
Output from the production segment is generated
using Monte Carlo simulation techniques. For culled
breeding cattle we simulate the number of infected cows and
bulls in a group of 40 such animals that would be presented
for slaughter. We use 40 head as a convenient count because
that's the capacity of most trucks that ar used to haul
cattle to slaughter. Each cow and bull is simulated as an
individual. The probability of infection is equal to the
product of herd prevalence and within-herd prevalence.
Within-herd prevalence varies according to an exponential
distribution. The only parameter in the exponential
distribution is the mean within-herd prevalence among all
infected herds.
Simulation of the production segment, when herd
prevalence and within-herd prevalence are set at their
lower, most likely, and upper bounds, produces the three
output distributions shown here. This output feed in to the
slaughter segment. Each of these distribution explain the
number of--that the number of fecal-shedding cattle varies
in any group of 40 head. On certainty regarding the true
distribution is reflected by the three different
distributions.
Looking at the most likely distribution, the
middle one of those three, the underlying true prevalence is
4 percent. For the lower-bound distribution the underlying
true prevalence is 3 percent. For the upper-bound
distribution the underlying true prevalence is 6 percent.
Greater than 90 percent of steers and heifers are
shipped directly from feedlots to slaughter plants without
going through livestock markets. Furthermore, these cattle
are usually slaughtered together in a lot, although they may
be mixed with one or more truckloads of cattle from the same
or another feedlot. The manner by which feedlot cattle are
marketed suggests they are more likely to be processed at
the slaughter plant in a clustered pattern. Clustering
implies that the infection status of a steer or heifer in a
slaughter plant is dependent on the lot it is in.
If we simulate the number of infected cattle per
truckload using the equation shown here, each truckload is
independently determined to be from an affected or non-
affected feedlot based on the herd prevalence. If the truck
is from an affected feedlot, then the number infected in the
truckload is determined based on the within-herd prevalence.
Again, within-herd prevalence varies according to the
exponential distribution.
This figure is the output for infected steers or
heifers in a truckload of 40 such animals presented for
slaughter. Upper and lower bounds result from uncertainty
regarding within feedlot and feedlot prevalence. In
contrast to the distribution for breeding cattle, this
distribution is skewed. It's most-likely value is zero
infected cattle in a truckload. Zero cattle can result
either because the truck originates from a non-affected
feedlot, or given that the truck originates from an affected
feedlot because the sample of 40 head from that feedlot
failed to contain any infected cattle.
Looking at the most likely distribution in this
graph, the underlying true prevalence of fecal shedders is
13 percent. For the upper and lower-bound distributions,
the underlying true prevalence of fecal shedders are 11 and
16 percent, respectively.
Herd or feedlot prevalence is assumed to be a
fixed but uncertain input to the production segment. In
other words, we assume there is some steady-state proportion
of herds at any given time that are affected. The lack of
evidence suggesting there are changes in the proportion of
the affected herds in the US over time supports this
assumption.
Seasonal changes in herd prevalence have been
reported, but these changes are probably the result of
seasonal changes in the within-herd prevalence for infected
herds. Herd or feedlot prevalence is a function of herd
sensitivity and the sampling data. Herd sensitivity is the
proportion of herds that test positive given the number of
samples collected per herd and the apparent within-herd
prevalence.
We used 5 studies to estimate the prevalence of
infected breeding herds. These studies were selected
because sampling was conducted across multiple states.
National studies on the occurrence of 0157 in breeding herds
have not shown any differences in prevalence between regions
of the country. Therefore, inferences drawn from the
selected studies are thought to be representative of US
breeding herd prevalence.
The Garber study is the largest study. It was
part of a national survey of the US dairy industry conducted
by the USDA. This survey collected fecal samples from 91
dairy herds across the US. Sampling was stratified for herd
size. To account for seasonal bias in sampling and
differences in sample size, we separately analyzed large and
small herd results from the survey.
The Hancock studies sampled dairy herds across
three northwestern states. In general, several monthly
sampling visits to each herd over 3, 6, or 12 months, were
made in these studies.
The final studies samples 15 cow/calf beef herds
across 5 midwestern states. This study was completed by
USDA-ARS researchers. In each herd 60 fecal samples from
weaned calves were collected.
The prevalence of affected feedlots is estimated
using these three studies. These studies include feedlots
that were sampled from multiple states. Because the
occurrence of 0157 in feedlots is assumed to not be
geographically clustered, inferences drawn from these
studies are also considered representative of the US feedlot
prevalence.
The largest study of 0157 occurrence in US
feedlots was conducted by USDA and reported by Dargatz. In
this study 100 feedlots with greater than a thousand-head
capacity were randomly selected throughout the US. In each
feedlot 120 fecal samples were collected for a determination
of apparent prevalence.
Another survey of 6 feedlots in Idaho, Oregon and
Washington was completed by Hancock. On average 180 samples
were collected from each feedlot.
Smith has recently reported results from
intensively sampling 5 midwestern feedlots. Over 600 fecal
samples were collected in each of these feedlots.
Herd prevalence is dependent on herd sensitivity.
We calculated herd sensitivity based on apparent within-herd
prevalence and the number of samples collected per herd. In
this figure p stands for the apparent within-herd prevalence
variable, and n stands for the average number of sample
collected in each herd. As implied by this equation, herd
sensitivity equals 1 minus the average or expected value of
the probability that no infected cattle would be detected in
a sample of n cattle. A herd sensitivity was calculated for
each of the studies we analyzed.
The herd and feedlot prevalent sampling evidence
implies apparent herd prevalence. To estimate the true herd
prevalence we used base theorem, which is the top equation
shown here. The likelihood function in base theorem is the
equation at the bottom of this slide. It shows that the
likelihood of herd prevalence is a function of the sampling
evidence and the herd sensitivity.
We derived these likelihood distributions for the
5 studies concerning herd prevalence. The Garber study was
stratified so there was actually 6 curves in this figure.
For each study the likelihood distribution reflects
uncertainty in true herd prevalence. Again, this
uncertainty is driven by the number of herd sampled in each
study, the number found positive, and the herd sensitivity.
The broadest and therefore more uncertain likelihood
distribution is that for the distribution labeled "1a."
This broad distribution results because herd sensitivity in
this case was calculated at 21 percent, which is so low that
a wide range of true herd prevalence levels are nearly
equivalently feasible. In contrast, the other curves
reflect increased certainty regarding herd prevalence levels
because herd sensitivity was much larger, ranging from 76 to
96 percent in these other studies.
The 5 likelihood distributions from the previous
slide are combined using base theorem. The resulting
distribution for herd prevalence is shown here. For our
most likely scenario, herd prevalence was set equal to the
expected value or average of this distribution. For the
upper and lower-bound scenarios, hers prevalence is equal to
the 90th or 10th percentiles of this distribution.
Likelihood distributions were also derived for the
three feedlot studies. In this analysis the herd
sensitivity was calculated as 77 percent, 86 percent and 99
percent for the Dargatz, Hancock and Smith studies,
respectively.
The Dargatz study's likelihood curve suggests the
most likely feedlot prevalence is somewhere around 85 to 90
percent. The other two studies, which sampled only 5 or 6
feedlots, suggest that the feedlot prevalence is most likely
around 100 percent.
The three likelihood distributions for feedlot
prevalence are also combined to from the distribution shown
here. Again, the most likely feedlot prevalence was set
equal to the average of this distribution. Upper and lower-
bound scenarios used feedlot prevalence levels equal to the
10th and 90th percentiles of this distribution.
Cattle sent to slaughter represent a special
subset of their respective herd populations. For example, a
cow culled from a dairy or beef herd may have a different
probability of being infected than a calf in that same herd,
or the prevalence of 0157-infected cattle about to be sent
to slaughter from a feedlot may be different from the
prevalence in cattle that have just been assembled to begin
feeding in that feedlot. In general, the research suggests
that there is a declining prevalence of cattle infection
with increasing age of cattle.
In our model we applied the within-herd prevalence
evidence that is most specific to cattle being sent to
slaughter. True within--or feedlot prevalence, is a
function of the sensitivity of the test used to diagnose
fecal shedding of individuals, as well as the apparent
prevalence observed in our studies that were presented.
The average breeding herd size is about 300 cows
per herd. Therefore, we assume a lower limit to within-herd
prevalence of one infected cow in 300. As a conceptual
model of 0157 infection in cattle herds, we assumed infected
cattle are colonized for a defined period. Research has
shown that a carrier state for 0157 in cattle is unlikely.
Nevertheless, there is evidence suggesting that cattle are
susceptible to reinfection following clearance of
colonization, and that cattle can be infected with one or
more strains of 0157 concurrently.
The average capacity of US feedlots is about 6,000
cattle per feedlot. Therefore, we assume the lower limit of
within-feedlot prevalence is one infected steer or heifer in
6,000. Additional assumptions introduced for breeding herds
also apply to feeding herds.
We used four studies to estimate the average
within-herd prevalence of infected breeding cattle in US
herds. These studies varied in their design, sampling and
laboratory methods. In combination, these studies' results
are assumed to represent a cross-sectional seasonally
average set of evidence for within-herd prevalence in US
breeding herds.
The Garber study was the USDA survey of the dairy
industry in which 22 positive herds were detected. The
Besser study was a year-long monitoring of 10 dairy herds in
Washington. Sampling detected 3 cow herds as infected in
that study. The Rice study took a convenient sample of cows
about to be culled from dairy herds enrolled in an Idaho,
Oregon and Washington survey. And the last study was a
survey of 25 cow/calf herds conducted by Hancock in
Washington, of which four were positive.
Four studies were used to estimate within-feedlot
prevalence. The first study was conducted by USDA and 63
positive feedlots were detected. That study was reported by
Dargatz. In a study of fecal prevalence in steers and
heifers at four slaughter plants, Hancock reported finding
5.8 percent of 240 cattle positive when sampling was done
just after the cattle were stunned in the slaughter plant.
In another study of feedlots in three northwestern states,
Hancock found all 6 feedlots sampled to contain at least 1
positive steer or heifer. These first three studies used
the same lab methods, and the most likely test sensitivity
was assumed to be 58 percent.
In the final study, Smith evaluated 5 midwestern
feedlots and found them all to contain a high proportion of
infected cattle. This study collected more samples in each
feedlot, collected larger samples of feces, and used a more
sensitive laboratory technique than the other three studies.
Test sensitivity was assumed to be 96 percent for this
study.
Within-herd prevalence varies from one infected
herd to the next. Furthermore, if we were to follow one
infected herd over the course of several months, we would
find that the prevalence of infected cattle within that herd
would vary.
The top graph of this slide is a histogram of
apparent within-herd prevalence from a study of post-weaned
heifers in 36 dairy herds. This graph implies an asymmetric
distribution for within-herd prevalence with the mode equal
to the lowest detectible level. Such a distribution shape
is consistent with a variable that fits in exponential
distribution. In the bottom figure the cumulative
probability distribution for this data and that predicted by
the exponential distribution are compared. A chi square
goodness of fit statistics supports the hypothesis that the
data conform to an exponential distribution.
In a national survey of milk cows and culled cows
in the US conducted by Garber, 22 infected herds were
infected. The cumulative probability distribution for
within-herd prevalence is depicted in this graphic. In this
case, goodness of fit analysis also supports the hypothesis
that these data fit an exponential distribution. The
exponential distribution has only one parameter, the mean or
average. By assuming that within-herd and within-feedlot
prevalence can be modeled using an exponential distribution,
we are left with the less difficult task of estimating the
average within-herd prevalence from the available data.
The preceding tables of data report apparent
within-herd or within-feedlot prevalence. To estimate a
distribution for the average, we used a method similar to
that already presented for herd prevalence. The only
difference for within-herd prevalence is that we used test
sensitivity, rather than herd sensitivity in the likelihood
function.
Lab methods varied between studies because of
different quantities of feces analyzed, different enrichment
broths, and different culture media used. Sanderson
evaluated lab methods and relative sensitivities were
presented. In this table we've interpolated and extended
Sanderson's results to incorporate methods not directly
studied in that study.
We used these results to model test sensitivity in
our analysis. Uncertainty regarding test sensitivity was
incorporated by inserting these data into a beta
distribution. We used the 10th and 90th percentiles from
these beta distributions as the lower and upper-bounds of
test sensitivity for the corresponding boundary analysis.
We took the means of these distributions for our most-likely
estimate of test sensitivity.
Test sensitivity is a function of lab methods and
the quantity of sample collected. To evaluate the Sanderson
sensitivity data further, we performed the analysis shown
here. In the two left-hand columns are displayed fecal
concentrations and their estimated frequencies among known
infected cattle. The three right-hand columns display the
probability that fecal samples of varying sizes would not
contain any organisms for the given fecal concentration. At
the very bottom of each of these columns then is the
probability that a sample of a given size would contain one
or more 0157 organisms.
From the Sanderson results we can compare the
relative sensitivity for the 0.1 and the 10-gram samples,
where the enrichment and plating media--the same enrichment
and plating media were used. For 10-gram samples 79 percent
of 24 positive cattle were found positive. Yet from the
analysis shown here, we expect 95.7 percent of samples from
infected cattle would contain at least one organism if 10-
gram samples were collected.
But by dividing 79 percent by 95.7 percent, we
find that this enrichment and plating media correctly found
83 percent of the samples containing at least one organism
to be positive. Similarly, for 0.1-gram samples, Sanderson
says that 58 percent of positive cattle were detected using
that sample size. Yet only 73.3 percent of positive fecal
samples would contain one or more organisms with that
sample. Therefore, 79 percent of the samples with one or
more organisms were in fact detected.
The reported sensitivity for this culturing system
is 80 percent for experimentally inoculated samples, that
the relative sensitivities measured for 0.1-gram and 10-gram
samples are consistent with this sensitivity after
adjustment for the probability that a sample contains at
least one organism is reassuring, in that it suggests that
the differences in relative sensitivity reported by
Sanderson for naturally-infected cattle incorporate the
effect of some samples not containing any organisms.
Therefore, we determined that no adjustments to the
Sanderson data seem necessary.
We derived these likelihood distributions for the
four studies of within-breeding-herd prevalence. The
likelihood distributions displayed here assume that the test
sensitivity is the average for the size of sample and lab
methods used in each study.
The likelihood distributions for the Garber and
Besser study are not much different. The Rice and Hancock
studies represent small data sets, and their likelihood
distribution suggests higher average within-herd prevalence.
The Hancock study cited here used the least sensitive
sampling methods, which increases the likelihood that many
test-negative cattle were theoretically infected.
The middle curve in this graph showed the
uncertainty regarding average within herd--breeding-herd
prevalence for culled breeding cattle in the most-likely
case. It was derived by combining the four likelihood
distributions from the previous slide. Lower and upper-
bound distributions were constructed similarly by changing
the test sensitivity for each study. The expected values of
these three distributions were used as the average within-
herd prevalence in the three scenarios we modeled.
We derived these likelihood distributions for true
within-feedlot prevalence for each of the four feedlot
studies. The outlier here is the Smith study. This study
includes a substantial amount of data. Consequently, this
likelihood distribution strongly influences our estimated
distribution for average within-feedlot prevalence.
The middle curve here is the most likely
distribution for average within-feedlot prevalence. It was
derived by combining the four likelihood distributions on
the previous slide. The upper and lower-bounds
distributions were similarly derived after changing the
test sensitivity. The expected values for each of these
distributions were used as the most likely and lower and
upper-bounds for average within-feedlot prevalence.
Statistics concerning the uncertain parameters of
this model are then summarized here. We estimate that the
great majority of breeding herds and feedlots contain at
least 0157-infected cattle. As you can see, herd
prevalence, our most likely estimate is 72 percent. For
feeding-herd prevalence, it's 88 percent.
DR. HULEBAK: Excuse me. If you're having trouble
following, it should be on page 19 of your handout.
DR. EBEL: We estimate that the great majority of
breeding herds contain at least 1 infected cattle. Also,
average within-feedlot prevalence is over twice as great as
average within-breeding-herd prevalence, a result which may
support that as cattle age, their likelihood of infection
does decline.
A quantitative link between prevalence of 0157 in
live cattle and the occurrence of contamination on carcasses
or in ground beef is limited. We are aware of only one
study, conducted in Great Britain, which has managed to show
an association between live cattle status and carcass status
for 0157. This study involved a limited number of animals
and much uncertainty attends its results.
Therefore, we believe quantifying the connection
between live cattle and carcass status is a critical
research need. The necessary research will serve to clarify
the importance of pre-harvest control in this food safety
problem.
The available evidence on the occurrence of 0157
in US cattle is substantial, but still limited. Moreover,
the results of studies on the occurrence and distribution of
this organism are in some cases different. The approach
we've used in handling this data is to incorporate
uncertainty about prevalence within each individual study
and between different studies. Additional uncertainty
regarding herd prevalence enters our model through
sensitivity parameters. These three elements of
uncertainty, within-herd, between study and sensitivity
combine to demonstrate our lack of complete comprehension of
0157 occurrence in US cattle populations.
Uncertainly regarding prevalence could be reduced
through additional large surveys of dairy cow/calf and
feeding herds. These additional surveys could improve on
those surveys cited here by increased sample sizes to
account for the low within-herd prevalence levels and
quantification of concentrations of 0157 in positive samples
to explain the levels of shedding detected. Nevertheless,
it is expected there will always be some uncertainty
regarding prevalence because definitive field surveys are
expensive and difficult to perform.
A great deal of speculation surrounds the role of
contaminated hides in the contamination of carcasses with
0157. Very little data is available on the proportion of
cattle whose hides are 0157-contaminated, and the
concentration of organisms on those hides. The reliability
and sensitivity of hide-testing methods needs to be
researched. Studies should also explore possible changes in
hide prevalence during transportation from the farm to
slaughter. Research on Salmonella has suggested that
prevalence increases dramatically during transportation.
Research is also needed on possible risk factors associated
with high contamination. Pen and/or housing design,
environmental sanitation practices and feed management are
all possible correlates.
There's considerable uncertainty regarding the
prevalence of cattle whose hides are contaminated with 0157.
In one study 1.7 percent of 240 feedlot cattle at four
slaughter plants had hair samples that were 0157 positive.
Paired fecal samples were collected from the animals in this
study, and no correspondence between fecal and hide status
was found.
Some researchers have hypothesized that the degree
of visible soiling of cattle hides or hair with mud, manure,
and/or bedding is correlated with microbial contamination of
carcasses, but this research has shown that the
concentration of generic E. coli organisms on carcasses
changes very little, whether the lot was composed of cattle
that had substantial hide soiling or the cattle were
relatively clean. The implication of this research is that
the role of 0157 hide contamination and carcass
contamination may not be correlated with visible clues.
Nevertheless, there is some indication in the research that
wetter cattle may result in carcasses with greater levels of
contamination.
Many studies of 0157 have tested the association
of hypothetical risk factors with the occurrence of 0157-
infected cattle. These studies have furthered our
understanding of the epidemiology of 0157 in cattle.
Nevertheless, there are still gaps in our knowledge. For
instance, factors which explain why some herds do not
contain 0157 await discovery. Risk factors that explain
seasonal patters in 0157 prevalence are still being
investigated. Also, the role of feed and water
contamination needs further study to be clarified.
Because risk factors will typically affect either
the herd or the within-herd prevalence of 0157, their
influence can be modeled by adjusting the prevalence
variables in this model relative to the baseline
distributions after we account for the frequency of the risk
factor among the population of herds or cattle.
There is a substantial amount of evidence
concerning the occurrence of 0157 in live cattle. In this
model our challenge was to coalesce this data into estimates
of herd and within-herd prevalence. As it's developed, the
model allows separation of variability from uncertainty.
Such a treatment is a significant improvement. As the
variability and uncertainty in this model's outputs are
propagated through the other segments of the risk
assessment, we will be capable of evaluating the importance
of the production segment and the occurrence of human
illnesses within the context of this uncertainty.
This is the end of my presentation. I'll be glad
to answer questions.
MS. OLIVER: Does the Committee have any questions
or comments, and all the experts too?
DR. HANCOCK: This is Dale Hancock, Washington
State University.
I wanted to ask a question about the herd
prevalence, particularly looking at the feedlot level. Just
on theoretical grounds, if the feedlot prevalence were--in
this estimate--80 something percent, as I recall, how would
there--since feedlots get cattle from a large number of
sources and feed, a number of loads of feed, that would be
the two logical primary ways of getting E. coli-0157 into a
feedlot, how would there be negative feedlots? Couldn't we
assume that the feedlot-herd prevalence is 100 percent, on
theoretical grounds?
DR. EBEL: I think that's reasonable as an
assumption. Empirically, that's more difficult to argue,
but theoretically that would be a reasonable argument or
hypothesis.
DR. HANCOCK: This is again Dale Hancock,
Washington State University.
Maybe the heterogeneity within feedlots that
wasn't modeled--and maybe it was, but you tell me--in the
cattle on feed study, the largest study that you reported
that had a 63 percent within-herd prevalence--or 63 percent
feedlot prevalence, excuse me--those cattle were clustered.
Those samples were clustered, because there were four pens
in each feedlot with 30 samples per pen, and at least the
empirical distribution in that stud of within-pen prevalence
was strongly skewed to the right, suggesting a big-pen
effect.
Could that account for the empirical estimate from
your models of the feedlot prevalence? I mean, was that
adequately modeled?
DR. EBEL: Well, I actually think it probably was
because we are looking at--we handled herds the same way--I
should say all the feedlots the same way, the distribution
of the sampling. Our determinant in estimating herd
prevalence is what was the apparent within-herd prevalence,
and of course, that's a sort of weighted estimate based on
using the results of both those pens that were shortest on
feed, the two that had a random draw from sort of the
middle, and then another sample from those that were on the
longest feed. So that if there's a bias in there it would
be in our inability to say that the estimate of within-herd
prevalence or apparent within-herd prevalence from those
feedlots is not weighted correctly, and to some degree, that
might be in the data because of the higher within-herd
prevalence in those pens that were shortest on feed. But
again, they represented one of the pens, and then we had two
that represented random draws, and then another from the
largest, so possibly--I should say the longest--so possibly
the longest and shortest had some canceling effect in terms
of our estimate of apparent within-herd prevalence, but to
some extent that might be true.
We did not explicitly try to account for that
clustering because our argument was or our assumption was
that across the four pens that were sampled on each herd, we
probably had a good estimate of apparent prevalence across
the entire feedlot.
DR. HANCOCK: This is Dale Hancock again.
To me, that's a decision that needs to be made, is
whether or not this very high--all the studies there
estimated very high feedlot prevalence, a very high percent
of feedlots had it, and to me, it is justifiable to assume
that the feedlot prevalence is 100 percent, but that's just
something to think about.
Before I quit her, I wanted to ask a question also
about the breeding herd prevalence, or the percent of
breeding herds that had it. There's an extreme
heterogeneity there, and I just want to make sure that we're
modeling that adequately. Just to give you a sense of that,
in that year-long study, '94, using relatively insensitive
methods, admittedly, over half of all of the positives
detected in that year-long study where they were sampled
monthly were detected on the single sampling date with the
most positives, and over 80 percent in the two sampling
dates, out of the roughly 12 per herd, with the most
positives, and generally in the warm months of the year.
And so it's extremely temporally clustered in
these herds, and in fact, over two-thirds of the sampling
dates in positive herds were associated to no positive
samples, herds that were eventually positive. Presumably it
was missed, or in the environment or not in cattle there.
So is there adequate modeling for this very
extreme level of temporal heterogeneity within these
positive herds?
DR. EBEL: Well, as we pointed out in the scope
presentation, at this point we are not incorporating
seasonality into the model, and our rationale is that
although there is some evidence in the live cattle research
concerning a seasonal pattern, we don't have the
corresponding evidence right now at the detail to sort of
link it up and evaluate its importance in the subsequent
segments: slaughter, preparation. So that's our
justification at this point. It's basically a simplifying
assumption.
To that extent then, the results from, say, a
year-long study, of course, represent our apparent look at
what the prevalence in those herds might be. Having made
the adjustment we have for sensitivity, we feel like we've
got a good picture, at least of average within-herd
prevalence on a seasonally average basis, but I think we
would all like to incorporate and feel like it is very
feasible to incorporate seasonality into the model. Our
precaution at this point has been basically that we don't
have data downstream of live cattle to really establish that
there in fact is a correlation, and as you will see as we
model into slaughter, we have sort of a proportionality
constant between live--incoming live prevalence and carcass
prevalence. And that if that's constant and isn't adjusted
for any sort of seasonal issues, it clearly is going to push
through a seasonal pattern into ground beef contamination
which may or may not be something we can empirically
demonstrate.
So until we get that data, that's been our reason
for being cautious and operating on sort of a seasonally-
average basis.
MS. OLIVER: Dane?
DR. HANCOCK: And can I make one final comment?
And this is for the record. I think what you've
shown here is accurate on the breeding-herd prevalence
versus feedlot prevalence, and your reasons for that, in my
view, are accurate, the age difference between those
animals. But it's important, I think, to make, for the
record, that those--it would be inaccurate to automatically
assume from that prevalence data the feedlots had to two to
three-fold higher prevalence, as I recall in your estimates,
that something about feedlot management is causing that
higher prevalence. Obviously, that's a good hypothesis that
needs to be looked at, but it has been looked at to a
certain degree, and there are several levels at which you
can look at it, but within a dairy herd, for example, we
have all age animals, and the--although the overall within-
herd prevalence is low because we have mostly older animals,
the prevalence within young stock within those herds is very
similar to prevalence within feedlots. And when we have
looked at dairy heifers in a dry-lot setting, because many
of the western dairies basically raise them in a feedlot
setting, their dairy heifers, compared to those that put
them on pasture, the prevalences are extremely similar. And
so we need to make certain that we don't infer that that two
to three-fold higher prevalence in feedlots is an effect of
feedlots rather than age, because it's very similar to the
age differences within dairy herds.
MS. OLIVER: Thank you. Dane.
DR. BERNARD: Thanks. Dane Bernard.
I'm glad Dale asked all those questions because
those were confusing to me as well, but I'm sure I'm the
only one in the group that is not a modeler, but in your
summary comments you mentioned that variability
uncertainty would be propagated throughout the model.
For my benefit, can you enlarge on what that means
and what its effect is?
DR. EBEL: Okay, thanks. What we've tried to do,
because this whole issue of variability and uncertainty is a
real large issue within the risk assessment community, but
isn't necessarily a similarly important issue for those
outside, is to try to find a compromise that we think works
for us, but basically we're taking and running throughout
the model three scenarios.
The first scenario is the most likely scenario,
and it's based on our best estimates of elements of--I
should say--yes, variability in the various segments of the
model. So the output we showed here for the most likely
scenario represents the output based on our best estimates
of what the average within-herd prevalence is, what we think
the best estimate is with regard to herd prevalence in the
corresponding parameters for feedlots. And we generate that
output at distribution, which, as we showed, is the number
of infected animals in say a group of 40, and that's the
output that then goes into slaughter for the most likely
scenario.
Then correspondingly we run two other scenarios
which we'll also put into slaughter. One is the lower-bound
and the other is the upper-bound, and they correspondingly
have higher estimated numbers of infected cattle in 40-head
or lower, depending on the bounds.
From the production, we are going to take those
lower bounds and put them into corresponding lower-bounds
for slaughter and upper-bounds, so that we'll end up having
three scenarios that sort of trail out and demonstrate
increasing uncertainty as we move progressively through the
model.
At the end we'll describe the upper and lower
bounds probablistically of what we might expect based on our
uncertainty in those inputs. And, again, that's the intent
of it.
DR. BERNARD: Dane Bernard, again. In layman's
terms, the greater the uncertainty at the beginning of the
model, that's going to affect the next analysis and the
uncertainty there as factored into the total uncertainty at
that point and right on through the model.
DR. EBEL: Right.
DR. BERNARD: Thank you.
DR. EBEL: It appears to increase as we go along.
MS. OLIVER: Mike Doyle?
DR. DOYLE: Thank you. This is Mike Doyle,
University of Georgia.
Eric, are we going to see any more about
production data?
DR. EBEL: Today, probably not.
DR. DOYLE: Okay. Well, back to my original
question. You came up with 11 percent shedding, and I
haven't seen any numbers that come up to 11 percent in this
presentation. So how do we get to 11 percent and a 102 to
103 per gram number of E coli being shed?
DR. EBEL: Okay. Well, again, as we pointed out,
the 102 or 103 was just actually taken from sort of an
expected value from I think the work you had done in calves
long ago. But it was just a place holder. We aren't
actually modeling contamination load per gram out of these
cattle. We're primarily interested in what's the prevalence
of cattle shedding.
But to get back to your first question about 11
percent, let's go to Slide 26 to show you the data that's
driving up estimates for feedlot cattle.
Anyway, the Smith data is certainly in excess of
11 percent. It turns out that as we combine this evidence,
make the adjustments for sensitivity, as you see the column
there listing average apparent prevalence, those would be
without making any adjustments for sensitivity of the test.
So those are all going to go up in addition to the Smith
data. As we go through the algorithm that we are obviously
just briefly touching on, that's the data that generates an
11 percent average within-herd prevalence.
Because we are modeling within-herd prevalence is
an exponentially distributed variable, however, that 11
percent is actually greater than what the median or the 50th
percentile of that distribution would be, because an
exponential is going to have a higher frequency at the lower
within-herd prevalence levels. That's just a function of
that distribution.
So I caution you to assume that that's the 50
percent break point, that 50 percent are greater than 11
percent and 50 percent or less than. It's actually 50
percent are going to be greater than some number less than
11 percent.
DR. DOYLE: Have you included the data that have
been reported in the press recently from USDA which has
these very high levels of carriage of 0157 by cattle?
DR. EBEL: Well, yes, I think we have to some
extent, although we're never quite sure what, you know, is
being referenced to what. But the Smith data is some very
recent data, and it is part of the information that's coming
out that's demonstrating much higher than previously report
prevalences.
Also, the Lagreid study out of ARS is a recently
published study, and their work continues. As they complete
things, we try to get that information. But as yet, some of
the information is not yet incorporated.
DR. DOYLE: Thank you.
DR. EBEL: Thanks.
MS. OLIVER: I'll take one more question now, and
that'll be from Mel Eklund, and then after that go to the
next presentation. If there's still more questions after
than and the Committee wants to during discussion, you can
ask them then.
DR. EKLUND: This is Mel Eklund from Seattle,
Washington.
Most of the questions I had have already been
answered by Dr. Hancock, but I have one other one that I
would like to ask. Since Dr. Hancock is here, maybe he
could answer it.
Have studies been done on cattle from rangelands,
like in Montana, where it takes--I grew up on a cattle ranch
there, and it takes 10 acres to raise one cow, and you have
a very widespread--and most of these animals in these areas
are--the breeding stock stays there, except sometimes bulls
are brought in, so you don't have a lot of influx of other
animals from these--have studies been done in these areas?
And there are feedlots that come from--in the Montana area
that come from these herds. I was just kind of curious what
the incidence might be in this type of environment.
DR. EBEL: Yes, as a matter of fact, the Lagreid
study, which, again, was recently reported--and I wanted to
flip to that to see if I can--there were 15 cow/calf herds.
Those were primarily range-type cow/calf herds were studied.
They didn't do any sampling of cows, fecal sampling of cows
in that study, so the data weren't appropriate for us to
bring into the within-herd prevalence estimate, but they do
show a fairly high prevalence of 13 out of the 15 herds that
they sampled--and, again, that was across five Midwestern
States, I believe--were found to contain at least one 0157
infected animal. But they sampled at-weaning calves and
that's the basis of their sample in that study.
DR. EKLUND: This is Mel Eklund again. Sometimes
you get into the Midwest areas, these are smaller acreages.
Some of the farms in Montana, you can drive 18 square miles
on them. I was just kind of curious what the incidence
might be in this environment.
DR. EBEL: When I say Midwestern, I mean--I grew
up in Illinois, and I call that Midwestern. But I guess I'm
thinking west of there. But they didn't--
DR. EKLUND: But that's small farms compared to
Montana.
DR. EBEL: Right, right. And yet the ones that
Lagreid worked in, they were in the Nebraska, Kansas type
area. But I don't know that they incorporated any Montana
herds in that study. Do you?
DR. HANCOCK: This is Dale Hancock. I don't know
about that. We've really only done one study where we
looked at cattle on range, and that was our earliest study
where our methods were the most primitive. But we did find
a really quite similar prevalence in range herds as in
cattle herds, and we reported on one instance actually in
West Texas where--and that's certainly an extensive type
system--where cattle and deer shared common sub-types of E.
coli 0157. And there's some recent work from Kansas, I
believe, on surface water transmission, and certainly we're
working on water trough transmission. And so there are
opportunities for transmission in that setting, it appears,
but there is a need for more data in the range setting.
MS. OLIVER: Thank you.
Our next presenter is Dr. Tanya Roberts, and she
will talk on the issues of slaughter, and Karen Hulebak will
discuss the questions that FSIS wants you to take into
consideration.
DR. HULEBAK: All right. When you listen, as you
listen to Dr. Roberts, please keep in your mind the
following questions:
What evidence would be necessary to satisfactorily
link, quantify the link between hide and carcass
contamination?
And, second, we have attempted to develop a
mechanistic model that follows product through the slaughter
plant. Would it be preferable to develop a strictly data-
anchored model that does not attempt to model processes
between monitoring points? If so, what data would be
required to develop such a model?
Excuse me. We're also going to try to help you
track along in your handouts with the overheads that we use
in these presentations. It's clear they don't track exactly
point to point, but we'll give you some guidance on where to
find a handout that more or less matches the projected
figure.
DR. ROBERTS: Actually, I have a few extras. A
lot of them have to do with some of the results we were able
to put in at the last minute.
It's a pleasure to be here to talk about the
slaughter segment of the E. coli 0157:H7 model. This slide
identifies who my other collaborators have been on the team
over the year and a half we've been working on it: Clare
Narrod, Scott Malcolm, Jennifer Kuzma, Bob Brewer, and Peter
Cowen. And we've also had comments from the other members
of the E. coli team that were working on other segments of
the model.
The outline is that I will discuss first the
overview of the model structure, that we're looking at what
kind of processes actually occur in slaughter plants.
Second, we'll go into a description of the kinds of pathways
that occur in the slaughter plant for 0157 contamination.
I'm going to discuss the event tree model
assumptions and the data that we used, and let me just take
a brief aside here that we tried to use in-plant data
wherever possible and not the laboratory studies, because we
were concerned that they wouldn't reflect actual operating
conditions. Whenever possible, we used national data, but
we did use some international data. We preferred E. coli
0157:H7 data rather than generic E. coli. And then, last,
I'm going to give you some final conclusions about the model
results and what the output is then to the next segment,
preparation.
In the slaughterhouse, as most of you know--but
not all of you work for the meat industry--live cattle enter
the slaughter plant from the farm. They go to the knock box
where they're stunned and bled, and they're hung on an
overhead rail. They go to the next part of the main floor
of the plant where the hide is removed, both mechanically
and manually. Then they go through the first
decontamination procedure to remove large fecal spots that
are on the carcass, and sometimes they have a carcass wash.
Evisceration is the next step in the procedure
where the gastrointestinal tract is removed. The next step
is the carcass is split with a large chain saw. You'll
notice that both this box and the knock box and stunning are
not color-coded. That's because we did not include them in
the model because the limited data that are available in the
literature show that they were relatively low risk. That's
something that we would welcome further data from, and we
would be happy to add them.
The next step in the process is the second
decontamination procedure. This is after the carcasses are
coming off the line and ready to go into the chiller, and in
the U.S., two processes are used. Mostly the larger plants
use a steam pasteurizer, and the smaller plants tend to use
various kinds of hot water carcass washes, with or without
the addition of various compounds.
Then the carcass goes into the chiller for one to
two days. It's taken out to the fabrication room where it's
cut up into steaks and roasts and chops, and the trim is put
into a combo bin or boxes, which then becomes the output to
the preparation segment.
You don't have this--no, I want to talk about
this. You don't have this slide in your handout, but I
thought maybe it would be useful to give you a little bit of
an overview of the kind of a structure that we used in the
slaughterhouse. We're using an event tree model, and we're
building it for each step in the slaughter process I showed
you on the previous slide. And we're looking at--each step
we ask: Can contamination occur during this procedure? And
this is a yes or no. If it can occur, then what are the
possible levels of possible contamination?
For each one of these events where you have
contamination and the levels of contamination, we ask what's
the probability that this will occur, and we use a
probability distribution to capture the variability and
uncertainty associated with that.
Then, finally, we use a Monte Carlo simulation to
take a random draw for each event in the tree, and we do
5,000 to 10,000 simulations depending on when you start to
get stable results.
So what we want to end up with is being able to
identify what the risk level is associated with different
pathways that we are developing in our event tree model, and
I'll discuss some of those pathways toward the end.
Next slide?
You also don't have this slide, but the point of
this slide is to give you sort of an overview of the kinds
of things that can go wrong in the slaughter process. These
are the things that we're trying to capture in our event
tree.
You could have a procedural failure, just a flawed
plan for a process. There is some new evidence that hasn't
been taken into account in an old operating procedure or
just an oversight. There could be an operator failure, and
those generally are of two kinds. One is the error of
commission, you do the wrong thing. You don't clean your
knife when you slit open the hide. Or it could be an error
of omission, you just forgot to do something. You
overlooked maybe one piece of equipment that you were
cleaning the night before in the sanitation procedure.
You could have equipment failure. An example of
this could be you could have a compressor that might fail in
the chiller, and normally you have a back-up, but maybe the
back-up failed. There could be a possibility here of
equipment failure. Or, as you heard in the previous talk,
you could have contaminated incoming product.
You do have this slide in your handout. For each
step in the slaughter plant, we model the process and the
pathway that could contribute to the risk, the sources of
data for the input, and then the model. So this is going to
be the similar structure that we're going to be talking
about for each one of the segments as we go through it.
As cattle enter the slaughter plant, they're
trucks in, and as you heard reports, there's a possibility
that they could have gastrointestinal contamination. They
could be a fecal shedder. So the model is broken into two
segments. We have one for steers and heifers and one for
breeding cattle, and the steers and heifers, the feeding
cattle, are modeled as one to five truckloads of 40 animals
that come from one feedlot, and they have similar GI tract
status on that feedlot. The breeding cattle, cows and
bulls, are modeled as independent animals with GI tract
status that's randomly picked from a national distribution
of the prevalence.
These are the slides that Eric showed you. This
is the one for steers and heifers, and looking at a
truckload of them, what's the probability that they'll be--
you know, how many fecal shedders are they likely to have in
that truckload. And he had the most likely, the lower-
bound, and upper-bound scenario. And this is the slide
you've seen before for a truckload of cows and bulls.
This summarizes the data on the previous two
slides, and it's the percent of cattle that are likely to be
infected by cattle type: the breeding herd with a 4 percent
most likely prevalence, and these are the upper and lower
bounds, and steer/heifer, 13 percent most likely prevalence.
We've got to get together on this, Eric, because I think you
said 11 was the most likely. We need to make some minor
adjustment in these numbers.
DR. HULEBAK: Tanya, excuse me. Is this in your
slide?
DR. ROBERTS: No. I don't think so, at any rate.
No. You do have this next one, though.
DR. POWELL: Mark Powell. This was an effort to
go back, review, and summarize the production that was being
outputted into the slaughter model.
DR. HULEBAK: So we don't have this slide.
DR. POWELL: It's already been shown in the
previous segment.
DR. HULEBAK: To the extent you can make note of
that, it would be helpful.
DR. ROBERTS: For each one? Okay.
This is the event tree for steers and heifers.
You have this truckload of 40 steers and heifers coming into
the slaughter plant, and they could either come from a
contaminated herd, so they have a possibility of the animals
on that truck being contaminated, and so you would get to
the individual animal basis so it has some probability of
going--of staying--of being contaminated and going up this
part of the event tree, or if the particular animal that's
being slaughtered isn't contaminated, it will continue down
this track. If the truck comes from an uncontaminated herd,
then no animals on the truck will be contaminated, and it
continues down this part of the event tree.
Once the animal is in the slaughter plant, then,
the first part that we include in the model is the dehiding,
and this is where the animal who has already been stunned
and bled and is now dead enters the main part of the plant.
It's upside down hanging from an overhead rail. Its hocks,
or feet, are removed. The bung, or rear end, is tied off.
The hide is cut down the midline, and the hide is pulled off
manually and mechanically with a variety of side pullers, up
pullers, and down pullers.
The pathway that could allow contamination can
occur via contact as the hide is removed with the
contaminated hide itself slapping back on the carcass, with
the worker's gloves or knives contaminating the carcass, or
you could have aerosol contamination that could be created,
especially if the hide puller moves rapidly and jerks the
animal around.
You have this slide, but it's been changed a
little bit. In the model part of the dehiding, we're going
to be looking at three things. One is the area that's
contaminated, the level of contamination, and on the next
slide, we'll be talking about the probability of
contamination.
The most likely scenario is that there are 3,000
cm2 of the carcass that can be contaminated during the
dehiding process, and this was the distribution--then we
used a distribution to characterize our uncertainty about
the exact size, and we have upper-bound and lower-bound
scenarios.
The level of contamination is 1 to 3 logs of
colony-forming units per carcass, and a Poisson distribution
was used to characterize the uncertainty.
The data that this is based on comes from the
combination of the FSIS carcass monitoring data that was
discussed earlier and the FSIS ground beef sampling data.
Next slide, please?
The third component of this dehiding model is this
probability of contamination, and here we relied on two
English studies. The one that we relied on the most is
Chapman--that was 1993--where they looked at a cattle
slaughter plant in South Yorkshire, and they were looking,
as I said, at cattle. But there was also an earlier study
by Howe et al. which looked at a calf operation where they
got similar contamination rates. Chapman was 30 percent;
the Howe et al. was 33 percent. So we thought the Howe was
sort of corroboration. And then we used this Chapman data,
and they found seven carcass positives out of 23 fecal
positives, so we put this into a beta distribution to
capture our uncertainty about the exact number that would be
contaminated.
The second part of the probability is to look at
the possibility that subsequent carcasses following a
fecally contaminated carcass could also be cross-
contaminated. In the Chapman study, this was 8 percent.
They found 25 fecal negatives that they tested that two of
them actually turned out to have positive carcasses. Since
they didn't contaminate themselves, they must have gotten
the contamination from someplace else, from one of the other
carcasses.
We used a geometric progression to capture this,
and the first animal then that follows a fecally
contaminated animal has a little over a 7 percent and the
second animal has a little less than 1 percent probability
of being contaminated.
Yes, you have this slide. So these are the event
trees. You have a GI-positive animal coming in. You have,
on average, a 30 percent chance that it will self-
contaminate itself and a 70 percent chance that it will not
contaminate its carcass as the hide is removed.
If you have a GI-negative animal that comes in,
we're looking at--if it does not follow a positive animal,
it stays negative, it has a negative carcass. If it follows
a positive animal, as I mentioned, we have--the next two
adjacent ones have some probability of becoming cross-
contaminated, but most of them will not be cross-
contaminated.
The next step in the slaughter model is the first
decontamination where we have knife trimming or spot steam
vacuuming that remove visible fecal contamination.
Sometimes this is also followed by a carcass rinse.
The pathway is that you can have removal of
0157:H7 if these procedures are effective, or you can just
redistribute it over the carcass. If the knife is not
cleaned in between cuts, it can transfer it from one
location on the animal to another. Or the water rinse
coming over can actually just move it physically down the
carcass rather than actually get it all the way off the
carcass. So we have both possibilities.
The model is based on data from two studies, Gill
and Dorsa. Gill found a 0.32-log reduction, the Dorsa study
found a 0.7-log reduction as their most likely values. So
what we did was we built a trapezoidal distribution around
this with a reduction of 0 to 1 log as being the whole
range.
Again, there are only a few studies that were
done, and it would be useful if we had more data here.
This shows you what the tree looks like. We have
this contaminated carcass that comes along, and it has a
possibility from a 0- to 1-log reduction with 0.3 and 0.7
being the most likely points here.
During a carcass evisceration, which is the next
step that's modeled in the slaughter plant model, the
process is that the GI tract and the rest of the organs are
removed. The possible pathway for contamination is that you
can have a rupture. You could have a knife nick, or there
could be some weakness in the GI tract because of maybe some
kind of an infection and it could rupture and come apart.
Now, it doesn't appear as though E. coli 0157:H7
is particularly likely to cause this. It's other organisms
that could cause this kind of a rupture, so whether the
animal's contaminated with 0157 is not likely to contribute
to the probability of a rupture.
The basis of our model actually comes from Bob
Brewer, one of our team members, who has extensive service
in FSIS in investigating slaughterhouses, and he suggested
that this self-contamination, this nick, could occur maybe
one in 100 times. The contamination level is assumed to be
equivalent to what we had in the dehiding earlier, and the
area contaminated is smaller. It just ranges from 1 to 100
cm2 with 25 cm being the most likely value.
So here you have this possibility of a positive
animal either rupturing or not rupturing. If the animal
didn't have any GI--any 0157 in its GI tract, it's going to
continue negative. Even if it had a rupture, it would not
cause contamination.
Next slide, please?
The next step in the model is to look at the
second decontamination procedure, and as I mentioned
earlier--oh, I see. We have carcass splitting in here,
don't we, in you guys' handouts? Well, we didn't model
that, so I left it out of these slides.
So moving on to Slide 16 in your handout, the
Carcass Decontamination II, the process here is that
decontamination methods are used to remove or kill 0157 from
the carcass exterior. At this point you have sides of beef
because it's already been sawed in half, and the two most
common techniques used by U.S. industry are steam
pasteurizer, in which these railed carcasses enter the steam
pasteurizer four at a time. This stainless steel clamshell
shuts around them. The air is blown in to blow the water
off the exterior of the carcass so that the steam can
penetrate, and steam of 180 to 210 degrees Fahrenheit is
applied for 5 to 15 seconds.
Most small and medium plants use a hot water wash,
although there are a few large plants that also use the hot
water wash instead of steam pasteurization. And here you're
using the heat as well as the volume of the water coming
over the carcass as methods of either dislodging or killing
the 0157. You also have the possible addition of organic
acids or trisodium phosphate. And the efficacy is going to
depend on the heat and the volume of water used.
The pathway is that you can have--where you can
have a change in the risk status is that the carcass wash is
going to either reduce or redistribute the organisms, and
the steam pasteurization can significantly reduce
contamination. However, low temperature use is not
effective.
The data that we actually put into the model for
steam pasteurization, we used a triangular distribution with
a range of 0 to 2 logs--this is based on Gill's work--and
with 1-log reduction the most likely.
For the hot water wash, what we did was we modeled
this the same way, that trapezoidal distribution, as we did
in the first decontamination procedure.
This shows the event tree pathways, then, for the
second decontamination. This shows the steam pasteurizer.
You have from a 0- to 2-log reduction with 1 log being the
most likely. And so you take a random draw from this if it
went through the pasteurizer to see what level of reduction
you actually got for the particular Monte Carlo simulation.
And, again, if it's an uncontaminated carcass, it goes
through this decontamination procedure, it's going to remain
uncontaminated.
The next step in the model is carcass chilling,
and the process here is that you have--sides of beef are
blast air chilled for 18 to 48 hours. The pathway is that
you can get growth or decline of E. coli 0157:H7 on the
carcass surface, and that's going to be a function of both
the time and the temperature. And you can also have cross-
contamination from other carcasses, and that's going to be
more likely the more crowded the chiller is.
In the model, we pooled data from three slaughter
plant studies, from Dorsa and from Gill and Bryant, to come
up with a common distribution, with a normal distribution,
where the mean is 0 and a standard deviation of 1.
This is the event tree. You can hardly see these
things. What we did was we assumed--and th