**Harvard Model of Bovine Spongiform Encephalopathy**

**Implications of Importing Cattle Over 30 Months of Age from Canada**

Joshua T. Cohen, Ph.D.

Center for the Evaluation of Value and Risk

Institute for Clinical Research and Health Policy Studies

Tufts New England Medical Center

June 22, 2007

[**Executive Summary** 3](#executive-summary)

[**1** **Introduction** 5](#introduction)

[**2** **Methods** 6](#methods)

[**2.1** **Revisions to the Harvard Simulation Model**
7](#revisions-to-the-harvard-simulation-model)

[**2.1.1** **Ambulatory Status (From Cohen and Gray (3))**
8](#ambulatory-status-from-cohen-and-gray-3)

[**2.1.2** **Operation of the *Antemortem* Inspector (From Cohen and
Gray (3))**
8](#operation-of-the-antemortem-inspector-from-cohen-and-gray-3)

[**2.1.3** **Tonsils (From Cohen and Gray (3))**
9](#tonsils-from-cohen-and-gray-3)

[**2.1.4** **SRM Inspection (From Cohen and Gray (3))**
9](#srm-inspection-from-cohen-and-gray-3)

[**2.1.5** **Supplemental Reports (From Cohen and Gray (3))**
10](#supplemental-reports-from-cohen-and-gray-3)

[**2.1.6** **Import of Infected Cattle** 10](#import-of-infected-cattle)

[**2.1.7** **Separate Slaughter and MBM Production for Dead and Healthy
Slaughter Cattle**
10](#separate-slaughter-and-mbm-production-for-dead-and-healthy-slaughter-cattle)

[**2.1.8** **Explicit Modeling of Recycling of Cattle Tissue in Poultry
Feed**
11](#explicit-modeling-of-recycling-of-cattle-tissue-in-poultry-feed)

[**2.2** **Base Case Assumptions** 11](#base-case-assumptions)

[**2.2.1** **Assignment of Ambulatory Status (From Cohen and Gray (3)
with Modifications)**
12](#assignment-of-ambulatory-status-from-cohen-and-gray-3-with-modifications)

[**2.2.2** ***Antemortem* Inspection (From Cohen and Gray (3) with
Modifications)**
13](#antemortem-inspection-from-cohen-and-gray-3-with-modifications)

[**2.2.3** **Infectivity in Tonsils (From Cohen and Gray (3))**
14](#infectivity-in-tonsils-from-cohen-and-gray-3)

[**2.2.4** **Feed Ban Compliance Rates (From Cohen and Gray (3) With
Modifications)**
15](#feed-ban-compliance-rates-from-cohen-and-gray-3-with-modifications)

[**2.2.5** **Consumption Rates for Bone-in-Beef (From Cohen and Gray
(3))** 17](#consumption-rates-for-bone-in-beef-from-cohen-and-gray-3)

[**2.2.6** **Disposition of Dead Animals**
18](#disposition-of-dead-animals)

[**2.2.7** **Disposition of Rendered Materials**
19](#disposition-of-rendered-materials)

[**2.2.8** **Food Inspector** 20](#food-inspector)

[**2.2.9** **The Misfeeding Rate** 20](#the-misfeeding-rate)

[**2.2.10** **Import of Infected Cattle**
20](#import-of-infected-cattle-1)

[**2.3** **Sensitivity Analyses (From Cohen and Gray (3) with
Modifications)**
24](#sensitivity-analyses-from-cohen-and-gray-3-with-modifications)

[**3** **Results and Discussion (Introduction From Cohen and Gray (3)
with Modifications)**
25](#results-and-discussion-introduction-from-cohen-and-gray-3-with-modifications)

[**References** 28](#references)

[**Appendix 1** **Base Case Parameter File Changes From Earlier
Analysis**
30](#appendix-1-base-case-parameter-file-changes-from-earlier-analysis)

[**Appendix 2** **Detailed Simulation Output**
30](#appendix-2-detailed-simulation-output)

[Appendix 3 Numerical Stability of Simulation Output
30](#appendix-3-numerical-stability-of-simulation-output)

#   {#section .JTC-Heading-1 .unnumbered}

# Executive Summary {#executive-summary .JTC-Heading-1}

The analysis described here has been conducted for USDA-APHIS, as a
component of a risk assessment conducted to evaluate the impact of
allowing additional cattle imports from BSE minimal-risk regions.
Specifically, this analysis supports the exposure assessment component
of the risk assessment and contributes to the environmental assessment.

In January 2005, USDA-APHIS published a final rule allowing some cattle
imports from countries falling into a new category, designated "minimal
risk regions". The rule places Canada into the minimal risk regions
category and allows that country to export cattle to the U.S., so long
as the animals are slaughtered prior to the age of 30 months. In order
to investigate the impact of allowing the import of older cattle from
Canada into the U.S. and eliminating the requirement that they be
slaughtered by a specified age, APHIS conducted a risk assessment. The
risk assessment estimates the likelihood that BSE-infected cattle will
be imported into the U.S. given the mitigations proposed (the release
assessment), the extent to which disease might spread among U.S. cattle
as a result (the exposure assessment), and characterizes the resulting
impacts (consequence assessment). This document supports the
environmental assessment and the exposure assessment component of the
risk assessment.

The analysis uses a computer simulation model developed for USDA by the
Harvard Center for Risk Analysis[^1]. A series of modifications to the
model have been implemented. The most important modifications for the
purpose of this analysis include enhanced capabilities for specifying
the import of infected cattle; and explicit modeling of cattle exposure
to the BSE agent via administration of poultry litter in cattle feed.
For the purpose of completeness, this report documents all modifications
to the function of the model and to its assumptions made since the
release of initial Harvard BSE risk assessment (1).

In order to characterize the impact of uncertainty, the analysis
evaluates the impact of using pessimistic assumptions for the rate of
mislabeling and contamination, the rate of on-farm misfeeding of
prohibited feed to cattle, the prevalence of various rendering
technologies used in the U.S., the proportion of poultry litter that is
used in cattle feed, and the prevalence of BSE in Canada.

Under base case conditions, the results of this analysis indicate that
the expected number of infected cattle in the U.S. over 20 years as a
result of importing cattle from Canada would amount to 21 animals. Most
of these infected animals (approximately 90%) would be imported
directly, while the remaining 10% would represent secondary infections
(*i.e.*, native U.S. cases). Potential human exposure over this 20-year
period would be expected to amount to 45 cattle oral ID~50~s. Of the
five uncertain parameters considered in the sensitivity analyses, the
model is most sensitive to the release of infectivity (as expressed by
BSE prevalence in Canada). Simultaneous assignment of pessimistic values
to all five of the uncertain assumptions considered here increases the
predicted number of secondary BSE cases to 42 on average, with total
potential human exposure increasing to 290 cattle oral ID~50~s over 20
years. Under all cases, the reproductive constant for BSE (R~0~) remains
less than 1 with high probability, indicating exponential growth in the
number of native U.S. cases following potential release from Canada is
unlikely. Equivalently, the results indicate that in the absence of a
continual release of BSE into the U.S., its prevalence will decrease
over time, eventually leading to its elimination. It is important to
note that this set of findings reflects the simultaneous use of
pessimistic values for a range of assumptions, including the assumptions
identified as being influential in earlier analyses (1), as well as the
assumed prevalence of BSE in Canada.

# 1 Introduction {#introduction .JTC-Heading-1}

The analysis described here has been conducted for the USDA Animal and
Plant Health Inspection Service (APHIS) as part of a risk assessment
conducted to evaluate the impact of allowing additional cattle imports
from BSE minimal-risk regions. Specifically, this analysis supports the
exposure assessment component of the risk assessment and contributes to
the environmental assessment.

In response to Canada's May 2003 discovery of a dairy cow in Alberta
province infected with bovine spongiform encephalopathy (BSE), the U.S.
Department of Agriculture (USDA) banned the import of cattle from that
country. The border closure reflected U.S. policy that prohibited such
imports from countries with indigenous cases of BSE. Since that initial
discovery, eight additional BSE-infected cattle from Canada have been
discovered, one of which had been exported to the U.S.

In January 2005, USDA-APHIS published a final rule allowing some cattle
imports from countries falling into a new category, designated "minimal
risk regions". The rule places Canada into the minimal risk regions
category and allows that country to export cattle to the U.S., so long
as the animals are slaughtered prior to the age of 30 months. In order
to investigate the impact of allowing the import of older cattle from
Canada into the U.S. and eliminating the requirement that they be
slaughtered by a specified age, APHIS conducted a risk assessment. The
risk assessment estimates the likelihood that BSE-infected cattle will
be imported into the U.S. given the mitigations proposed (the release
assessment), the extent to which disease might spread among U.S. cattle
as a result (the exposure assessment), and characterizes the resulting
impacts (consequence assessment). This document supports the
environmental assessment and the exposure assessment component of the
risk assessment.

The analysis described here uses the BSE simulation developed by the
Harvard Center for Risk Analysis (HCRA) (1;2). Using this model, the
analysis estimates the extent to which the release of BSE from Canada
will contribute to the prevalence of BSE among cattle in the U.S. In
addition to describing the contribution to prevalence, the model also
describes the ability of safeguards in the U.S. to eliminate BSE in
terms of the disease's reproductive constant, designated R~0~. The R~0~
parameter is the average number of new BSE cases resulting from each
existing BSE case. A value of R~0~ greater than 1.0 indicates that
prevalence increases over time (because each existing case gives rise to
more than one new case on average), while a value of R~0~ less than 1.0
indicates that prevalence decreases over time. The HCRA BSE model
reports other information, including the potential for human exposure to
the BSE agent.

Strictly speaking, an analysis of the impact of eliminating age
restrictions on cattle imports from Canada would compare the proposed
regulation to the status quo, which, as noted above, allows the import
of cattle from Canada, so long as they are slaughtered prior to the age
of 30 months. This analysis develops a more conservative "bounding"
estimate of the incremental impact of eliminating age restrictions on
animal health and human exposure because it does not "subtract" risks
associated with status quo policies.

In addition to the base case scenario, this analysis presents
sensitivity analyses to evaluate the impact of alternative plausible
assumptions identified in earlier work (1;2) as having the most
important impact on the simulation results. In addition to evaluating
the impact of these assumptions, this assessment also investigates the
impact of assigning pessimistic values to the proportion of poultry
litter used in cattle feed, and to the prevalence of BSE in Canada.

The remainder of this paper has two sections. Section 2 describes the
methodology, including parameter assumptions and revisions to the
Harvard simulation model for this project. Section 3 details results and
discusses the findings.

# 2 Methods {#methods .JTC-Heading-1}

The BSE simulation model used in this analysis was first described by
Cohen *et al.* (1;2). The functionality of that model has since been
modified to accommodate the requirements of an analysis conducted for
the USDA Food Safety Inspection Service (FSIS) to evaluate regulatory
changes that influence the risk pathway. Section 2.1 describes the
modifications, as well as additional modifications that have been made
for this analysis. The base case scenario builds on the base case
analysis described by Cohen *et al.* (1) and subsequently revised for
FSIS in Cohen and Gray (3). Section 2.2 describes the base case scenario
for this analysis representing conditions in the U.S. if imports of
older cattle from Canada are allowed. Finally, Section 2.3 describes
sensitivity analyses conducted for the purpose of characterizing the
extent to which the findings of this analysis depend on assumptions made
for critical parameters.

Note that the analysis conducted for FSIS represents a "layer" of
simulation and parameter revisions made on top of the original BSE model
described by Cohen *et al.* in October 2003 (1). The analysis described
here therefore represents a second layer of simulation and parameter
revisions. In order to save the reader the trouble of having to refer
back to both of the previous documents and sequentially reconstructing
the series of changes described, this document effectively subsumes the
relevant changes to the FSIS report (3) and incorporates text from that
report as needed to describe the changes that were made as part of that
effort. Where appropriate, this document indicates where material has
been taken verbatim or with some modifications from that report.

Finally, note that, as described in the introduction to the methodology
section in the FSIS report, this analysis uses central estimates for the
base case assumptions where possible. However, in the few limited cases
where doing so is not feasible, this analysis attempts to err on the
side of using assumptions that overstate the extent to which BSE might
spread in the U.S. (*i.e.*, so-called "conservative" assumptions). To
the extent that the findings here show that release of BSE into the U.S.
does not pose a substantial risk, the use of these conservative
assumptions does not compromise the qualitative implications of this
report's findings. In any case, use of conservative values was largely
limited to parameters that, based on earlier work (1), are known not to
have a substantial impact on the simulation results.

## 2.1 Revisions to the Harvard Simulation Model {#revisions-to-the-harvard-simulation-model .JTC-Heading-2}

Eight sets of changes to the Harvard simulation model have been
implemented for the purpose of this and other analyses conducted since
the release of the assessment conducted by Cohen *et al.* (1). This
section describes those changes. These include changes made for FSIS
(3): addition of ambulatory status as a characteristic that factors into
*antemortem* inspection findings (Section 2.1.1); changes to the
operation of the *antemortem* inspection process (Section 2.1.2);
addition of tonsils as a tissue category (Section 2.1.3); changes to SRM
inspection (Section 2.1.4); addition of supplemental reports that detail
contamination of human food by animal age and ambulatory status (not
used in this report) (Section 2.1.5); and changes made specifically for
APHIS in this analysis: enhanced capabilities for specifying the import
of infected cattle over the course of the simulated period (Section
2.1.6); separate specification of SRM inspector, rendering, and
disposition of rendered material (MBM transport) for healthy slaughter
and dead animals (Section 2.1.7); and explicit modeling of cattle
exposure to the BSE agent via administration of poultry litter in cattle
feed (Section 2.1.8). Sections 2.1.1 through 2.1.5 are taken directly
from Cohen and Gray (3), and the remaining three sections are new.

It should be emphasized that not all of these capabilities are used in
all analyses, and in particular, this analysis uses only a subset of the
capabilities described here. Section 2.2 describes the specific
assumptions made for this analysis.

### 2.1.1 Ambulatory Status (From Cohen and Gray (3)) {#ambulatory-status-from-cohen-and-gray-3 .JTC-Heading-3}

USDA now prohibits the use of non-ambulatory animals for human food. In
order to represent this policy in the simulation model, along with
others that may place restrictions on the use of these animals in feed,
the simulation has been modified so that it tracks the ambulatory status
of cattle infected with BSE. The simulation can designate an animal as
non-ambulatory when the animal becomes infected with BSE or when the
animal develops clinical signs of BSE. Once an animal becomes
non-ambulatory, it cannot become ambulatory at a later time during the
simulation. This framework is consistent with non-ambulatory status
being assigned to an animal at *antemortem* inspection. Appendix 1
details the assignment of parameter values to control this feature.

### 2.1.2 Operation of the *Antemortem* Inspector (From Cohen and Gray (3)) {#operation-of-the-antemortem-inspector-from-cohen-and-gray-3 .JTC-Heading-3}

Tasks performed by the *antemortem* inspector are now divided into two
steps. As part of the first step, inspection, the *antemortem* inspector
determines 1) whether the animal passes inspection based on
considerations not related to BSE, and 2) whether the animal shows
clinical signs of BSE. As part of the second step, allowed use
designation, the *antemortem* inspector designates the animal as allowed
for use in human food, animal feed, or both feed and food based on the
two determinations made in the inspection step, and on the animal's
ambulatory status.

*Inspection*: The *antemortem* inspector makes two judgments. First, it
determines if the animal passes or fails inspection based on
considerations not related specifically to the manifestation of clinical
BSE signs. The probability that an animal will pass inspection based on
non-BSE considerations depends on 1) its ambulatory status, and 2) its
age. The second determination made by the *antemortem* inspector is
whether the animal displays clinical signs of BSE. This finding depends
on the animal's ambulatory status and on whether the animal is, in fact,
clinical. Note that it is possible for the inspector to fail to identify
a clinical animal as displaying BSE signs. That is, this feature makes
false negative findings possible.

*Allowed use designation*: The *antemortem* inspector follows two sets
of deterministic rules, one of which governs whether an animal can be
used in human food, and the other which governs when an animal can be
used in animal feed. In both cases, the designation depends on three
factors: 1) whether the animal passed inspection for non-BSE related
factors, 2) whether the *antemortem* inspector identified the animal as
displaying clinical signs of BSE, and 3) whether the animal is
non-ambulatory.

Appendix 1 details the assignment of parameter values to control the
behavior of the *antemortem* inspector. As configured for the analyses
described in this report, the *antemortem* inspector prohibits use of
cattle tissue in feed only if the animal displays clinical signs of BSE.
Although in reality, there is no such explicit requirement governing
*antemortem* inspection, this characterization of the *antemortem*
inspector's operation makes sense within the context of the simulation
model. In particular, the simulation explicitly models only animals that
have been infected with BSE. Moreover, the base case assumes that only
animals that have reached the clinical stage of disease display clinical
signs consistent with BSE. In the "real world," such animals would be
most likely tested for the BSE agent after slaughter and would test
positive with very high probability (because they have reached the end
of the incubation period and because the screening tests are geared to
minimize false negative results). After testing positive, the carcasses
from such animals would be destroyed. That is, as is effectively assumed
in the simulation, the tissue from such animals could not be used in
either human food or in animal feed.

### 2.1.3 Tonsils (From Cohen and Gray (3)) {#tonsils-from-cohen-and-gray-3 .JTC-Heading-3}

Tonsils have been added as a tissue category.

### 2.1.4 SRM Inspection (From Cohen and Gray (3)) {#srm-inspection-from-cohen-and-gray-3 .JTC-Heading-3}

The original BSE simulation model (1) eliminated infectivity using the
SRM inspector only when animals were sent to slaughter. That is, the SRM
ban did not apply to dead stock. The model has been revised so that it
can remove infectivity from dead stock, as well.

### 2.1.5 Supplemental Reports (From Cohen and Gray (3)) {#supplemental-reports-from-cohen-and-gray-3 .JTC-Heading-3}

The simulation model can now report distributions for the number of
cattle oral ID~50~s in human food (by tissue) for cattle by age range
and ambulatory status. As now configured, the simulation can create
separate reports for each combination of the following age ranges (0 to
11 months, 0 to 23 months, 0 to 29 months, 30+ months, and all ages) and
ambulatory status designations (normal, non-ambulatory).

### 2.1.6 Import of Infected Cattle {#import-of-infected-cattle .JTC-Heading-3}

The original model allowed the import of infected cattle only once, at
the beginning of the simulation. The model now allows for the periodic
import of infected cattle. The model allows the specification of the
animal type (dairy, beef slaughter, or reproductive beef), gender, age
at import, the import rate for infected animals (i.e., the Poisson
distribution parameter), the age at infection, and optionally, a
scheduled slaughter age. This last parameter can be used to specify that
animals must be slaughtered within a fixed amount of time after import.
Alternatively, if no specific slaughter age is specified, the simulation
assimilates the animals into the U.S. cattle population and slaughters
them at random using the appropriate probabilities specified for native
U.S. cattle.

### 2.1.7 Separate Slaughter and MBM Production for Dead and Healthy Slaughter Cattle {#separate-slaughter-and-mbm-production-for-dead-and-healthy-slaughter-cattle .JTC-Heading-3}

The revised simulation model now allows for the specification of
distinct parameter values governing SRM inspection, rendering, and
disposition of processed proteins for dead and healthy slaughter cattle.
The revision was made to allow for the possibility that the segment of
the rendering industry that processes dead animals is distinct from the
segment of the industry that processes healthy slaughter animals. In
addition, it is possible that the effectiveness of SRM removal for
healthy slaughter animals will differ from the corresponding
effectiveness for dead cattle.

Because the model allows distinct specification of rendering, SRM
inspection and processed tissue disposition for dead and healthy
slaughter cattle, a feature has been added that allows non-ambulatory
cattle to be treated like either dead cattle or like healthy slaughter
animals. If the user chooses to treat non-ambulatory cattle like dead
cattle, tissue from these animals will not be available for human
consumption. In addition, 1) the SRM inspector, renderer, and MBM
transport parameters for dead animals will govern processing of
non-ambulatory animals; 2) non-ambulatory cattle will be disposed of on
the farm with the same probability as animals that die on the farm; and
3) the use of such cattle in animal feed is subject to restrictions
imposed by *antemortem* inspection. If the user chooses to treat
non-ambulatory animals like healthy slaughter animals, then the tissue
from these animals can be used in human food (as well as in animal
feed), depending on assumptions specified for the *antemortem*
inspection[^2]. In addition, the SRM inspector, renderer, and MBM
transport parameters for healthy slaughter animals will govern
processing of non-ambulatory animals.

Finally, if the user chooses to treat non-ambulatory cattle like healthy
slaughter animals, tissue from these animals may be available for use in
human food, subject to constraints imposed by *antemortem* inspection.

### 2.1.8 Explicit Modeling of Recycling of Cattle Tissue in Poultry Feed {#explicit-modeling-of-recycling-of-cattle-tissue-in-poultry-feed .JTC-Heading-3}

The simulation model allows the user to specify the proportion of the
MBM produced that is used in poultry feed (separate values can be
specified for dead and healthy slaughter cattle), and the proportion of
poultry litter that is administered to cattle. The model assumes that
100% of infectivity in poultry feed ends up in poultry litter. As a
result, the proportion of MBM infectivity that ends up in cattle feed
via this pathway is the product of the proportion of MBM sent to poultry
feed producers, and the proportion of poultry litter that is used in
cattle feed.

## 2.2 Base Case Assumptions {#base-case-assumptions .JTC-Heading-2}

This section outlines changes made to the base case assumptions used in
the earlier risk assessment (1) and implemented in the current analysis.
Revisions discussed include those related to the assignment of
ambulatory status (Section 2.2.1), those related to *antemortem*
inspection (Section 2.2.2), assumptions regarding the amount of
infectivity in tonsils (Section 2.2.3), assumptions related to the level
of compliance with the feed ban (Section 2.2.4), new assumptions
regarding the use of animals for the generation of T-bone steaks and
other uses of bone-in-beef (Section 2.2.5), new assumptions regarding
the disposition of dead animals (Section 2.2.6), new assumptions
regarding the disposition of rendered products (Section 2.2.7),
modifications to the assumed proportion of various tissues made
available for human consumption (Section 2.2.8), and new assumptions
regarding misfeeding (Section 2.2.9). Section 2.2.10 describes the
assumed release of infected cattle into the U.S. in the base case
scenario.

### 2.2.1 Assignment of Ambulatory Status (From Cohen and Gray (3) with Modifications) {#assignment-of-ambulatory-status-from-cohen-and-gray-3-with-modifications .JTC-Heading-3}

The revised model now requires specification of ambulatory status
probability conditional on whether an animal displays clinical signs of
disease. For animals that show no signs, this analysis assumes that the
probability of being non-ambulatory, designated
$P(\text{NA}|\text{NS})$, is the same as the unconditional probability
of being non-ambulatory, designated *P(NA)*. This latter probability is
simply the proportion of cattle in the entire population that are
non-ambulatory.

Although data are not currently available, this analysis assumes
approximately 1 in 200 animals is nonambulatory. That is, it is assumed
that P(NA) is 0.5% and hence that $P(\text{NA}|\text{NS})$ is 0.5%. As
shown in Cohen and Gray (3), this assumption has only a limited impact
on the simulation results.

The probability that animals with clinical BSE signs are non-ambulatory,
designated $P(\text{NA}|S)$, can be calculated using Bayes formula. In
particular

  -------- ------------------------------------------------------------------------------------------------ -------
           $P(\text{NA}|S) = \frac{P(S|\text{NA})P(\text{NA})}{P(S|\text{NA})P(\text{NA}) + P(S|A)P(A)}$,   \(1\)

  -------- ------------------------------------------------------------------------------------------------ -------

where $P(S|\text{NA})$ is the probability that an animal displays
clinical BSE signs given that it is non-ambulatory, and $P(S|A)$ is the
probability it displays clinical signs given that it is ambulatory. The
most extensive BSE compliance data have been collected in Europe (4).
However, the European surveillance data do not document ambulatory
status. Cohen and Gray (3) investigated a range of values for
$P(\text{NA}|S)$ ranging from 8% (base case) to as high as 100% (see
Sensitivity Analysis 8 in that report). Their analysis showed that the
value assigned to this parameter had only a minor impact on the
simulation results.

### 2.2.2 *Antemortem* Inspection (From Cohen and Gray (3) with Modifications) {#antemortem-inspection-from-cohen-and-gray-3-with-modifications .JTC-Heading-3}

*Probability of passing inspection for non-BSE factors* -- For animals
with normal ambulatory status, this analysis uses the pass probabilities
used by Cohen *et al.* (1). It is assumed here that non-ambulatory
animals do not pass *antemortem* inspection. Note that Cohen *et al.*
(1) showed that the simulation results are not sensitive to assumptions
about the performance of the *antemortem* inspector. In addition, as
described below, this analysis assumes that material derived from
non-ambulatory animals cannot be used in human food. On the other hand,
non-ambulatory status does not affect use of tissue in animal feed.

*Probability that antemortem inspector will discover a clinical animal*
-- Cohen *et al.* (1) assumed that the *antemortem* inspector passes
(i.e., fails to successfully identify) 10% of all animals with clinical
signs of BSE. That is, that report assumed *antemortem inspection*
identifies clinical animals with 90% probability. This analysis assumes
that it is more difficult for inspectors to identify non-ambulatory
animals as having BSE because there is no opportunity to observe their
movements. As a result, it is assumed here that the *antemortem*
inspector identifies clinical animals as showing BSE signs with 95%
probability if the animal is ambulatory, and with 85% probability if the
animal is non-ambulatory. That is, non-ambulatory animals with clinical
signs are more difficult to discover than clinical animals that are
still ambulatory.

*Antemortem rules for use of animals in human food* -- In the base case,
an animal can be used in human food so long as it is ambulatory and
passes both aspects of the *antemortem* inspection -- *i.e.*, 1) the
animal must pass the inspection for non-BSE factors, and 2) the
inspector does not identify the animal as showing clinical BSE signs.

*Antemortem rules for use of animals in animal feed* -- In the base
case, an animal can be used to produce animal feed so long as the
inspector does not identify the animal as showing clinical BSE signs.

*Disposition of Nonambulatory Animals* -- As noted in Section 2.1.7, the
simulation allows the user to specify that non-ambulatory animals be
treated like animals that die prior to being sent to slaughter. In this
case, the animals are disposed of "on the farm" with the same
probability as dead animals. When disposed of on the farm the BSE agent
in the carcass cannot contaminate either animal feed or human food. When
non-ambulatory animals are not disposed of on the farm, this option
causes them to be processed in the same way that dead animals are
processed when they are not disposed of on the farm (SRM removal rates,
rendering reduction technologies, and disposition assumptions for
rendered product). However, this analysis assumes that non-ambulatory
animals are treated like healthy slaughter animals, although the
disposition of the resulting materials is subject to limitations imposed
by *antemortem* inspection rules.

### 2.2.3 Infectivity in Tonsils (From Cohen and Gray (3)) {#infectivity-in-tonsils-from-cohen-and-gray-3 .JTC-Heading-3}

Recent information suggests that bovine tonsils may carry BSE
infectivity (5). A pathogenesis study found that inoculating the brains
of calves with tonsil tissue from BSE-infected cattle successfully
transferred the disease. Specifically, one out of five calves inoculated
intra-cerebrally (i.c.) with tonsil from animals 10 months post
infection developed BSE. No other time points (6, 18, or 21 months post
infection) have resulted in inoculated calves developing BSE (5;6).

The Scientific Panel on Biological Hazards of the European Food Safety
Authority estimated from the results of the pathogenesis study that a 50
gram tonsil would contain no more than 0.005 bovine oral ID~50~s. An
analysis by Det Norske Veritas (DNV), using a different assumption for
the differential effectiveness of *i.c.* *vs.* oral exposure, estimated
the infectivity in a 50 gram tonsil to be approximately 0.25 bovine oral
ID~50~s (cited in (5)). The corresponding total in a pair of tonsils is
0.5 bovine oral ID~50~s.

Assuming an incubation period of 36 months, which has been typical in
the pathogenesis study, this analysis estimates that at 10 months post
infection (when non-zero infectivity in tonsils was observed), total
infectivity in an animal to be approximately 250 cattle oral ID~50~s
(see Cohen *et al.* (1)). Hence, the total infectivity in tonsils
implied by the DNV calculations amounts to 0.2% of the total infectivity
in the entire animal (0.5 ÷ 250 oral ID~50~s). This analysis assumes
that the tonsils maintain this same fraction of infectivity throughout
the BSE incubation period. Given the extremely small proportion of
infectivity estimated to be present in tonsils, this simplifying
assumption can have at most a negligible impact on the simulation
results. In order to maintain the same total quantity of infectivity in
an animal assumed by Cohen *et al.* (1), this analysis multiplies the
tissue-specific fractions for other tissues at each age point by 99.8%.

### 2.2.4 Feed Ban Compliance Rates (From Cohen and Gray (3) With Modifications) {#feed-ban-compliance-rates-from-cohen-and-gray-3-with-modifications .JTC-Heading-3}

This analysis uses government surveillance data to estimate
probabilities for mislabeling and contamination in MBM and feed
production facilities. Mislabeling occurs when a renderer or feed
manufacturer incorrectly labels prohibited product as non-prohibited.
Contamination occurs when MBM or feed not labeled as containing a
prohibited product is tainted with prohibited product. Contamination can
occur in mixed facilities (facilities that manufacture product
containing prohibited material and product designated as not containing
prohibited material on the same production line) and is presumably made
worse by incomplete cleanout procedures when production is switched from
prohibited to non-prohibited product.

Since the publication of Harvard's November, 2001 BSE risk assessment
(7), additional information on compliance with the 1997 feed rule has
become available. The U.S. FDA Center for Veterinary Medicine (CVM) has
collected and disseminated the state and FDA inspection results for
facilities that handle prohibited material (*i.e.*, ruminant derived
protein, with some exceptions). This information (see
<http://www.accessdata3.fda.gov/BSEInspect>) quantifies the number of
facilities out of compliance with the feed rule and hence serves as a
useful starting point for this report's analysis. However, because the
U.S. FDA databases do not report the size of these facilities (*i.e.*,
total material throughput), an assumption must be made regarding the
size of the non-compliant facilities compared to other facilities. For
this purpose, this analysis assumes that the non-compliant facilities
are the same size on average as facilities not cited for feed rule
violations. This assumption is likely to be conservative because
inspectors report that smaller firms are more likely to be cited for
violations of various sorts than larger ones (personal communication,
Neal Bataller, FDA/CVM, May, 2004).

In order to estimate mislabeling and contamination probabilities, this
analysis relies on data collected by FDA/CVM[^3] prior to September
2003. FDA/CVM data collected prior to September 2003 better detail the
nature of the violations discovered, reporting the total number of firms
with at least one violation and designating each violation as a case in
which: 1) products were not labeled as required, 2) the facility did not
have adequate systems to prevent co-mingling, or 3) the facility did not
adequately follow record keeping regulations. More recent data report
violations only in terms of the type of action indicated -- *i.e.*,
Official Action Indicated (OAI), Voluntary Action Indicated (VAI), or No
Action Indicated (NAI). FDA (8) defines these terms[^4].

Table 1 reproduces the April 2002 FDA Update (9), the most recent
summary reported prior to the September, 2003 change in database and
reporting details. The data summarized here are limited to facilities
handling prohibited materials.

**Table 1**

**April, 2002 Results of Inspections at Facilities Handling Prohibited
Materials**

+---------------------+--------+--------+---------+---------+---------+
|                     | *      | *      |         | **Cited |         |
|                     | *Inspe | *Cited |         | for     |         |
|                     | cted** | for    |         | Commin  |         |
|                     |        | M      |         | gling** |         |
|                     |        | islabe |         |         |         |
|                     |        | ling** |         |         |         |
+---------------------+--------+--------+---------+---------+---------+
| **Facility Type**   | *      | *      | **Pe    | **(N)** | **Pe    |
|                     | *(N)** | *(N)** | rcent** |         | rcent** |
+---------------------+--------+--------+---------+---------+---------+
|                     |        |        |         |         |         |
+---------------------+--------+--------+---------+---------+---------+
| Renderers           | 171    | 4      | *       | 3       | *       |
|                     |        |        | *2.3%** |         | *1.8%** |
+---------------------+--------+--------+---------+---------+---------+
|                     |        |        |         |         |         |
+---------------------+--------+--------+---------+---------+---------+
| Feed mills          |        |        |         |         |         |
+---------------------+--------+--------+---------+---------+---------+
| > Licensed Feed     | 370    | 8      | 2.2%    | 2       | 0.5%    |
| > Mills             |        |        |         |         |         |
+---------------------+--------+--------+---------+---------+---------+
| > NL Feed Mills     | 1224   | 55     | 4.5%    | 28      | 2.3%    |
+---------------------+--------+--------+---------+---------+---------+
| > Total             | 1594   | 63     | *       | 30      | *       |
|                     |        |        | *4.0%** |         | *1.9%** |
+---------------------+--------+--------+---------+---------+---------+
|                     |        |        |         |         |         |
+---------------------+--------+--------+---------+---------+---------+
| Other Firms^(a)^    | 2153   | 77     | 3.6%    | 34      | 1.6%    |
+---------------------+--------+--------+---------+---------+---------+

Notes:

\(a\) Other firms include ruminant feeders, on-farm mixers, protein
blenders, and distributors

Use of data collected prior to the December 23, 2003 discovery of a BSE
case in Washington state is likely to produce conservative compliance
estimates because compliance rates have most likely improved in the wake
of that discovery. For example, June, 2005 FDA compliance data (10)
indicate that only 1.1% of rendering firms (2 of 176) were cited for any
OIA violation. For feed mills, the corresponding figure was 0.1% (3 of
2,331).

The parameters adopted for this report's analysis are shaded in Table 1
and reproduced in Table 2 for the purpose of comparing them with
assumptions made in the earlier risk assessment (1).

**Table 2**

**Assumptions for Mislabeling and Contamination**

  ---------------- --------------- ---------------- ------------ ---------------- --------------- ---------------- ------------ ----------------
                   **MBM                                                          **Feed                                        
                   Production**                                                   Production**                                  

                   **2003^(a)^**                    **This                        **2003^(a)^**                    **This       
                                                    Analysis**                                                     Analysis**   

  **Parameter**    **Base Case**   **Pess-imistic   **Base       **Pess-imistic   **Base Case**   **Pess-imistic   **Base       **Pess-imistic
                                   Case**           Case**       Case**                           Case**           Case**       Case**

                                                                                                                                

  Probability of   14%             25%              1.8%         14%              16%             16%              1.9%         16%
  Contamination                                                                                                                 

                                                                                                                                

  Proportion of    0.1%            1%               0.1%         0.1%             0.1%            1%               0.1%         0.1%
  Prohibited                                                                                                                    
  Material                                                                                                                      
  Transferred to                                                                                                                
  Non-Prohibited                                                                                                                
  Material per                                                                                                                  
  Contamination                                                                                                                 
  Event                                                                                                                         

                                                                                                                                

  Mislabeling      5%              10%              2.3%         5%               5%              33%              4%           5%
  Probability                                                                                                                   
  ---------------- --------------- ---------------- ------------ ---------------- --------------- ---------------- ------------ ----------------

Notes:

\(a\) Values from Cohen et al. (1).

Although the base case parameter values reflect several conservative
assumptions, Cohen and Gray (3) showed that even substantial
modifications to these rates have at most a modest impact on the
simulation results (see Sensitivity Analysis #1 in that report). It is
therefore likely that any conservative impact resulting from these
assumptions would likewise be modest.

### 2.2.5 Consumption Rates for Bone-in-Beef (From Cohen and Gray (3)) {#consumption-rates-for-bone-in-beef-from-cohen-and-gray-3 .JTC-Heading-3}

Cohen *et al.* (1) assumed that slaughter facilities do not produce
bone-in cuts of beef from animals over 24 months of age[^5]. These cuts
are potentially important because they may contain spinal cord, dorsal
root ganglia (DRG) or both. At the request of USDA-FSIS (3), and based
on the judgment of USDA-FSIS personnel, this analysis has revised these
assumptions to reflect use of bone-in cuts of beef from animals 24
months of age and over. In particular, this analysis assumes that for
all animals 12 months of age and older, 30% of spinal cord ends up in
bone-in-beef (category "bone") when the spinal cord is not removed
during processing. This analysis also assumes that for all animals, 30%
of DRG is available for potential human exposure as a result of
consuming bone-in-beef (category "bone"). These uses may include
specific cuts of beef like T-bone steaks and other uses of these bones,
including soup and stock production. The use of spinal cord of cattle 30
months of age and older is banned from human consumption (Federal
Register / Vol. 69, No. 7 / Monday, January 12, 2004 / Rules and
Regulations).

### 2.2.6 Disposition of Dead Animals {#disposition-of-dead-animals .JTC-Heading-3}

Table 3 details recent estimates of the proportion of animals that are
rendered among downers and those that die prior to slaughter.

**Table 3**

**Proportion of Animals Rendered Among Downers and Those That Die Prior
to Slaughter**

+------------------------+----------------------+---------------------+
| **Category**           | **Total Mortality    | **Proportion        |
|                        | and Downers**        | Rendered**          |
+------------------------+----------------------+---------------------+
|                        |                      |                     |
+------------------------+----------------------+---------------------+
| **Informa Economics    |                      |                     |
| (11)**                 |                      |                     |
+------------------------+----------------------+---------------------+
| Older cattle           |                      |                     |
+------------------------+----------------------+---------------------+
| > Dairy                | 584,550              | 62%                 |
+------------------------+----------------------+---------------------+
| > Feedlot              | 300,000              | 94.4%               |
+------------------------+----------------------+---------------------+
| > Beef cows            | 1,025,750            | 20.7%               |
+------------------------+----------------------+---------------------+
| > Total                | 1,910,300            | 44.7%               |
+------------------------+----------------------+---------------------+
| Calves                 | 2,365,600            | 27.4%               |
+------------------------+----------------------+---------------------+
| Calves and older       | 4,275,900            | 35.1%               |
| cattle                 |                      |                     |
+------------------------+----------------------+---------------------+
|                        |                      |                     |
+------------------------+----------------------+---------------------+
| **U.S. FDA (12), Table |                      |                     |
| 2**                    |                      |                     |
+------------------------+----------------------+---------------------+
| All dead cattle under  | 2,365,000            | 5%                  |
| 500 pounds             |                      |                     |
+------------------------+----------------------+---------------------+
| Dead cattle from       | 300,000              | 90%                 |
| feedlots               |                      |                     |
+------------------------+----------------------+---------------------+
| Beef cows              | 1,400,000            | 10%                 |
+------------------------+----------------------+---------------------+
| Dairy cows             | 400,000              | 60%                 |
+------------------------+----------------------+---------------------+
| Total                  | 4,465,000            | 17%                 |
+------------------------+----------------------+---------------------+

In response to comments on its initial analysis and the differences
between FDA and Informa estimates, FDA substituted new industry data
into the analysis and revised its estimate from 17 percent to 33 percent
with an upper bound of 42%. FDA acknowledges uncertainty in the
estimates. This analysis adopts U.S. FDA's estimate that 42% of cattle
that die prior to slaughter are rendered (p. 58,588 in (13)). This value
is considered by FDA to be an upper bound for this parameter.

### 2.2.7 Disposition of Rendered Materials {#disposition-of-rendered-materials .JTC-Heading-3}

The 2003 risk assessment (1) assumed that approximately 30% of
prohibited MBM and non-prohibited MBM is either exported or used in pet
food and hence is not available to contaminate domestic cattle feed.
While 15% to 30% of MBM produced in the U.S. has typically been
exported, that proportion dropped substantially in 2004, to 5% of
production. Demand abroad for poultry by-product from the U.S. has
remained relatively strong. This analysis therefore assumes that 95% of
domestically produced prohibited MBM remains in the U.S. and is
available for use in animal feed. For non-prohibited product, this
analysis assumes that 70% remains in the U.S. and is available for use
in animal feed. Table 4 details the assumed disposition of MBM by type
of renderer and type of product.

**Table 4**

**Disposition of MBM**

+--------------------------------+-----+-----+-----+-----+-----+-----+
|                                | **T |     |     |     |     |     |
|                                | ype |     |     |     |     |     |
|                                | of  |     |     |     |     |     |
|                                | Re  |     |     |     |     |     |
|                                | nde |     |     |     |     |     |
|                                | rer |     |     |     |     |     |
|                                | and |     |     |     |     |     |
|                                | T   |     |     |     |     |     |
|                                | ype |     |     |     |     |     |
|                                | of  |     |     |     |     |     |
|                                | MB  |     |     |     |     |     |
|                                | M** |     |     |     |     |     |
+--------------------------------+-----+-----+-----+-----+-----+-----+
|                                | **P |     | *   |     | *   |     |
|                                | roh |     | *No |     | *Mi |     |
|                                | ibi |     | n-P |     | xed |     |
|                                | ted |     | roh |     | T   |     |
|                                | I   |     | ibi |     | ype |     |
|                                | ngr |     | ted |     | Ing |     |
|                                | edi |     | I   |     | red |     |
|                                | ent |     | ngr |     | ien |     |
|                                | R   |     | edi |     | t** |     |
|                                | end |     | ent |     |     |     |
|                                | ere |     | R   |     | **R |     |
|                                | r** |     | end |     | end |     |
|                                |     |     | ere |     | ere |     |
|                                |     |     | r** |     | r** |     |
+--------------------------------+-----+-----+-----+-----+-----+-----+
|                                | **  | **  | **  | **N | **  | **N |
|                                | P** | NP^ | P** | P** | P** | P** |
|                                |     | (b) |     |     |     |     |
|                                |     | ^** |     |     |     |     |
+--------------------------------+-----+-----+-----+-----+-----+-----+
| P Feed Producer (excluding     | 50% | 50% | N   | 50% | 50% | 50% |
| poultry feed)                  |     |     | A^( |     |     |     |
|                                |     |     | a)^ |     |     |     |
+--------------------------------+-----+-----+-----+-----+-----+-----+
| NP Feed Producer               | 0%  | 10% | N   | 10% | 0%  | 10% |
|                                |     |     | A^( |     |     |     |
|                                |     |     | a)^ |     |     |     |
+--------------------------------+-----+-----+-----+-----+-----+-----+
| Mixed Feed Producer            | 5%  | 10% | N   | 10% | 5%  | 10% |
|                                |     |     | A^( |     |     |     |
|                                |     |     | a)^ |     |     |     |
+--------------------------------+-----+-----+-----+-----+-----+-----+
| Poultry Feed Producer          | 40% | 0%  | N   | 0%  | 40% | 0%  |
|                                |     |     | A^( |     |     |     |
|                                |     |     | a)^ |     |     |     |
+--------------------------------+-----+-----+-----+-----+-----+-----+
| Out (Unavailable to U.S.       | 5%  | 30% | N   | 30% | 5%  | 30% |
| Cattle)                        |     |     | A^( |     |     |     |
|                                |     |     | a)^ |     |     |     |
+--------------------------------+-----+-----+-----+-----+-----+-----+

Abbreviations: P -- prohibited, NP -- non-prohibited

Notes: (a) This analysis assumes no product from a non-prohibited
renderer is labeled as prohibited

\(b\) Prohibited ingredient renderers may produce feed that is
mislabeled as non-prohibited.

Finally, of the infectivity that ends up in poultry feed and ultimately
in poultry litter (see Section 2.1.8), this analysis assumes that 1% is
used in cattle feed, close to an estimate reported by American Proteins,
Inc. (personal communication from Kevin Custer, Vice President of
American Proteins, to Lisa Ferguson, USDA APHIS, November 15, 2005).

### 2.2.8 Food Inspector {#food-inspector .JTC-Heading-3}

For animals 30 months of age and older, this analysis assumes that 1% of
SRMs are potentially available for human consumption (brain, spinal
cord, dorsal root ganglia \[DRG\], gut, eyes, AMR-derived meat, bone,
and trigeminal ganglia \[TGG\]). This estimate is based on FSIS data on
compliance with the regulations related to Specified Risk Materials
(SRMs)
(http://www.fsis.usda.gov/Fact_Sheets/BSE_Rules_Being_Strictly_Enforced/index.asp).

### 2.2.9 The Misfeeding Rate {#the-misfeeding-rate .JTC-Heading-3}

Cohen *et al.* (1) assumed that 1.6% of correctly labeled prohibited
feed was administered to cattle (the misfeeding rate), and that the
worst case value for this parameter is 15%. More recent data
(www.ngfa.org/article.asp?article_id=5460, last viewed June 5, 2005)
indicate that the worst case value for this parameter is 5%, i.e., that
a rate of 15% is unrealistically pessimistic.

### 2.2.10 Import of Infected Cattle {#import-of-infected-cattle-1 .JTC-Heading-3}

This section describes the assumed base case release of infected cattle
into the U.S. as the result of importing bovine livestock from Canada.

*Assumed Release of Infected Cattle*

The release assessment quantifies the rate at which cattle are imported
annually from 2007 through 2026 in each of the following categories:
slaughter cattle (steers and heifers, cows, bulls and stags, and
calves); stockers/feeders; and breeding cattle.

For the purpose of developing parameter input files, each of these
groups must be described in terms of their type (BEEF, BEEFREPRO, or
DAIRY), gender (MALE or FEMALE), age (in months), the annual rate at
which infected animals in this group are imported (Poisson distribution
parameter), age at infection (months), age to be slaughtered (or no age,
indicating that the imported animals are to be integrated into the U.S.
cattle population). Table 5 divides each of the groups defined in the
release assessment into groups with each of these characteristics
defined. Note that when these groups are divided across genders or into
different age groups, the proportions in the two right-most columns are
used to apportion the total. The values in Table 5 represent the assumed
import rates for the base case scenario for the year 2007. For other
years, the total import rate for animals in the release group is
different, resulting in different estimates for the rate at which
infected animals are imported. All other entries in the table are the
same for other import years. Finally, note that the age groups
identified represent a discrete characterization of what is in reality a
more continuous set of values. For example, steers and heifers aged 17,
18, and 19 months old are imported, in addition to those aged 16 or 20
months old (groups specified in Table 5).

**Table 5**

**Apportionment of Release Assessment Import Groups**

<table>
<colgroup>
<col style="width: 17%" />
<col style="width: 9%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
</colgroup>
<tbody>
<tr class="odd">
<td><strong>Release Assessment Group</strong></td>
<td><strong>Type</strong></td>
<td><strong>Gender</strong></td>
<td><p><strong>Age</strong></p>
<p><strong>(Months)</strong></p></td>
<td><strong>Annual Import Rate</strong></td>
<td><strong>Age at Infection (Months)</strong></td>
<td><strong>Age at Slaughter (Months)</strong></td>
<td><strong>Total Imported in Release Group</strong></td>
<td><strong>Gender Fraction</strong></td>
<td><strong>Age Fraction</strong></td>
</tr>
<tr class="even">
<td>SLAUGHTER</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Steers and Heifers</td>
<td>BEEF</td>
<td>MALE</td>
<td>16</td>
<td>0.01</td>
<td>1</td>
<td>16</td>
<td>727,802</td>
<td>60%</td>
<td>5%</td>
</tr>
<tr class="even">
<td></td>
<td>BEEF</td>
<td>MALE</td>
<td>20</td>
<td>0.13</td>
<td>1</td>
<td>20</td>
<td>727,802</td>
<td>60%</td>
<td>45%</td>
</tr>
<tr class="odd">
<td></td>
<td>BEEF</td>
<td>MALE</td>
<td>24</td>
<td>0.13</td>
<td>1</td>
<td>24</td>
<td>727,802</td>
<td>60%</td>
<td>45%</td>
</tr>
<tr class="even">
<td></td>
<td>BEEF</td>
<td>MALE</td>
<td>30</td>
<td>0.01</td>
<td>1</td>
<td>30</td>
<td>727,802</td>
<td>60%</td>
<td>5%</td>
</tr>
<tr class="odd">
<td></td>
<td>BEEF</td>
<td>FEMALE</td>
<td>16</td>
<td>0.01</td>
<td>1</td>
<td>16</td>
<td>727,802</td>
<td>40%</td>
<td>5%</td>
</tr>
<tr class="even">
<td></td>
<td>BEEF</td>
<td>FEMALE</td>
<td>20</td>
<td>0.10</td>
<td>1</td>
<td>20</td>
<td>727,802</td>
<td>40%</td>
<td>50%</td>
</tr>
<tr class="odd">
<td></td>
<td>BEEF</td>
<td>FEMALE</td>
<td>24</td>
<td>0.08</td>
<td>1</td>
<td>24</td>
<td>727,802</td>
<td>40%</td>
<td>40%</td>
</tr>
<tr class="even">
<td></td>
<td>BEEF</td>
<td>FEMALE</td>
<td>30</td>
<td>0.01</td>
<td>1</td>
<td>30</td>
<td>727,802</td>
<td>40%</td>
<td>5%</td>
</tr>
<tr class="odd">
<td>Cows</td>
<td>BEEFREPRO</td>
<td>FEMALE</td>
<td>60</td>
<td>0.02</td>
<td>1</td>
<td>60</td>
<td>115,424</td>
<td>100%</td>
<td>20%</td>
</tr>
<tr class="even">
<td></td>
<td>BEEFREPRO</td>
<td>FEMALE</td>
<td>72</td>
<td>0.04</td>
<td>1</td>
<td>72</td>
<td>115,424</td>
<td>100%</td>
<td>50%</td>
</tr>
<tr class="odd">
<td></td>
<td>BEEFREPRO</td>
<td>FEMALE</td>
<td>84</td>
<td>0.02</td>
<td>1</td>
<td>84</td>
<td>115,424</td>
<td>100%</td>
<td>30%</td>
</tr>
<tr class="even">
<td></td>
<td>DAIRY</td>
<td>FEMALE</td>
<td>36</td>
<td>0.02</td>
<td>1</td>
<td>36</td>
<td>173,136</td>
<td>100%</td>
<td>20%</td>
</tr>
<tr class="odd">
<td></td>
<td>DAIRY</td>
<td>FEMALE</td>
<td>46</td>
<td>0.06</td>
<td>1</td>
<td>46</td>
<td>173,136</td>
<td>100%</td>
<td>50%</td>
</tr>
<tr class="even">
<td></td>
<td>DAIRY</td>
<td>FEMALE</td>
<td>72</td>
<td>0.04</td>
<td>1</td>
<td>72</td>
<td>173,136</td>
<td>100%</td>
<td>30%</td>
</tr>
<tr class="odd">
<td>Bulls and stags</td>
<td>BEEFREPRO</td>
<td>MALE</td>
<td>66</td>
<td>0.04</td>
<td>1</td>
<td>66</td>
<td>53,658</td>
<td>100%</td>
<td>100%</td>
</tr>
<tr class="even">
<td>Vealers/light calves</td>
<td>BEEF</td>
<td>MALE</td>
<td>4</td>
<td>0.02</td>
<td>1</td>
<td>4</td>
<td>51,286</td>
<td>70%</td>
<td>100%</td>
</tr>
<tr class="odd">
<td></td>
<td>BEEF</td>
<td>FEMALE</td>
<td>4</td>
<td>0.01</td>
<td>1</td>
<td>4</td>
<td>51,286</td>
<td>30%</td>
<td>100%</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>BREEDING</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Dairy cows/heifers</td>
<td>DAIRY</td>
<td>FEMALE</td>
<td>17</td>
<td>0.03</td>
<td>1</td>
<td>none (b)</td>
<td>49,560</td>
<td>100%</td>
<td>100%</td>
</tr>
<tr class="odd">
<td>Beef cows/heifers</td>
<td>BEEFREPRO</td>
<td>FEMALE</td>
<td>17</td>
<td>0.00</td>
<td>1</td>
<td>none (b)</td>
<td>4,909</td>
<td>100%</td>
<td>100%</td>
</tr>
<tr class="even">
<td>Bulls</td>
<td>BEEFREPRO</td>
<td>MALE</td>
<td>20</td>
<td>0.00</td>
<td>1</td>
<td>none (b)</td>
<td>3,087</td>
<td>100%</td>
<td>100%</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>STOCKER/FEEDER</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>All</td>
<td>BEEF</td>
<td>MALE</td>
<td>12</td>
<td>0.09</td>
<td>1</td>
<td>17</td>
<td>189,139</td>
<td>70%</td>
<td>100%</td>
</tr>
<tr class="even">
<td></td>
<td>BEEF</td>
<td>FEMALE</td>
<td>12</td>
<td>0.04</td>
<td>1</td>
<td>17</td>
<td>189,139</td>
<td>30%</td>
<td>100%</td>
</tr>
</tbody>
</table>

Notes

\(a\) The scaled import rate is equal to the product of 1) the annual
import rate for the release group (e.g. 727,802 for steers and heifers
in 2007); 2) the gender fraction; 3) the age fraction; AND 4) the BSE
prevalence rate 0.68 x 10^-6^ in the base case, which equals the
expected value for prevalence calculated using the Bayesian birth cohort
method UK feed ban data).

\(b\) These animals are integrated into the U.S. cattle population and
hence have no definitive age at slaughter. Each month, they might be
slaughtered, depending on the slaughter probability assigned their type,
age, and gender.

## 2.3 Sensitivity Analyses (From Cohen and Gray (3) with Modifications) {#sensitivity-analyses-from-cohen-and-gray-3-with-modifications .JTC-Heading-2}

As in the 2003 risk assessment (1), this assessment includes a series of
univariate analyses to identify potentially important assumptions. These
assumptions are conducted by holding all but one set of assumptions
equal to their base case values. The set of assumptions to be evaluated
are set equal to pessimistic values to see if doing so influences key
model predictions -- in particular, for this analysis, the predicted
number of cattle infected with BSE in the U.S. over a 20-year period.

The sensitivity analyses conducted here evaluate the impact of
alternative assumptions for specific parameters identified as
influential in the original analysis (1). This assessment also
investigates other assumptions, as described below. The goal of this
sensitivity analysis was to investigate the extent to which alternative
plausible assumptions might increase the estimated risk associated with
importing cattle from Canada.

Sensitivity analyses include:

-   *Sensitivity 1* -- Mislabeling and contamination -- This analysis
    revises the base case values for these parameters to take into
    account new data on compliance rates. The sensitivity analyses
    evaluate the impact of these revisions by using the previous base
    case values from Harvard's October, 2003 report as the worst case
    values in the current analysis. In particular, it increases the
    mislabeling rates to 5% for both MBM and feed production. This
    sensitivity analysis increases contamination rates to 14% (MBM
    production) and 16% (feed production) (see Section 2.2.4).

-   *Sensitivity 2* -- Misfeeding -- The base case value for this
    parameters is 1.6%. This analysis investigates the impact of using
    the pessimistic value of 5% for this parameter (see Section 2.2.9).

-   *Sensitivity 3* -- The render reduction factor -- This analysis
    changes the distribution of render reduction factors using the worst
    case assumptions for this parameter from Harvard's October, 2003
    report.

-   *Sensitivity 4* -- The proportion of poultry litter used in cattle
    feed. The base case value for this parameter is 1%. The sensitivity
    analysis investigates use of 5% for this parameter.

-   *Sensitivity 5* -- The prevalence of BSE in Canada -- In place of
    the base case prevalence of 0.68 x 10^-6^ (the expected value for
    prevalence calculated using the Bayesian birth cohort method UK feed
    ban data), the sensitivity analysis uses a value of 3.9 x 10^-6^
    (the expected value calculated using the BSurvE Prevalence B
    estimate without including feed ban data).

-   *Sensitivity 6* -- All assumptions from Sensitivity Analyses 1, 2,
    3, 4, and 5. In this analysis, all 5 parameters analyzed in the
    preceding sensitivity analyses are set to their pessimistic values.

# 3 Results and Discussion (Introduction From Cohen and Gray (3) with Modifications) {#results-and-discussion-introduction-from-cohen-and-gray-3-with-modifications .JTC-Heading-1}

Detailed results of this report's analysis appear in Appendix 2.
Appendix 2A summarizes the overall results from each simulation,
including epidemic statistics (number of animals infected, *etc*.),
frequency of different modes of infection, frequency for different modes
of death (natural death *vs.* slaughter), the flow of infectivity
through the rendering and feed production system, and potential human
exposure to the BSE agent (cattle oral ID~50~s) by tissue type. Appendix
2B contains a series of 12 graphs for each simulation scenario.

The graphs and tables in Appendix 2 summarize distributions for each of
the model's output values. Note that the distributions for each scenario
arise as the result of modeled stochastic phenomena corresponding to
that scenario's assumptions. For example, the base case scenario assumes
that 5% of the rendering facilities do not reduce infectivity levels
(*i.e.*, they have a render reduction factor of 1.0). However, the
proportion of BSE-infected animals actually sent to such facilities
varies from simulation trial to simulation trial. As a result of this
and other factors that differ from trial to trial, the trial-to-trial
results vary, even though the underlying assumptions (in this case, the
proportion of animals sent to each type of rendering facility [on
average]{.underline}) remain the same. Because many of the underlying
assumptions are likewise uncertain, this assessment includes sensitivity
analyses (see Section 2.3). For example, the 95^th^ percentile estimate
for potential human exposure in the base case provides an upper end
estimate for this parameter assuming the base case assumptions are
valid. However, the sensitivity analyses describe the range of
predictions for the number of BSE cases and potential human exposure
values associated with alternative plausible assumptions.

Further documentation of the Appendix 2 tables appears in Appendix 3C of
Cohen *et al.* (1), although there is one change to the tables in
Appendix 2A. Added to the values listed under the "Epidemic Statistics"
heading, these tables now list an estimate of R~0~, the epidemic's basic
reproduction rate (14). Essentially, the value of R~0~ is the average
number of animals that become infected as the result of each new
infected case. If R~0~ is greater than 1.0, the prevalence of the
disease tends to grow over time. If it is smaller than 1.0, prevalence
tends to decrease over time and eventually, the disease dies out.
Section 1.2 of Gray and Cohen (15) explains the estimation of R~0~. In
brief, this value is estimated as the number of animals that become
infected with BSE (excluding the infected animals introduced through
import) divided by the number of BSE-infected animals that die during
the simulation.

Tables 6a and 6b summarize key results for the base case and sensitivity
analyses, showing how alternative (pessimistic) assumptions affect the
predicted number of additional new cases of BSE and total human exposure
to the BSE agent over the 20-year simulation period.

**Table 6a**

**Total Number of New Infected Cases of BSE During the 20 Years
Simulation Period**

  --------------------------------------------------------------------------------------------------------------
                                           **Percentiles**                                          
  ----------------------------- ---------- ----------------- ------------ ------------ ------------ ------------
  **Scenario**                  **Mean**   **5^th^**         **25^th^**   **50^th^**   **75^th^**   **95^th^**

  Base Case                     21         12                16           19           22           30

  S1 -- Mislabel and contam.    21         12                16           19           22           30

  S2 -- Misfeeding              23         12                16           19           23           42

  S3 -- Render reduction factor 21         12                16           19           23           30

  S4 -- Poultry litter          22         12                16           19           23           38

  S5 -- Canadian BSE Prevalence 120        92                100          110          120          180

  S6 -- All assumptions         150        99                110          130          160          270
  --------------------------------------------------------------------------------------------------------------

**Table 6b**

**Total Potential Human Exposure to BSE (Cattle Oral ID~50~s) During the
20 Year Simulation Period**

  --------------------------------------------------------------------------------------------------------------
                                           **Percentiles**                                          
  ----------------------------- ---------- ----------------- ------------ ------------ ------------ ------------
  **Scenario**                  **Mean**   **5^th^**         **25^th^**   **50^th^**   **75^th^**   **95^th^**

  Base Case                     45         1.6x10^-5^        0.0056       0.041        0.83         260

  S1 -- Mislabel and contam.    48         7.9x10^-6^        0.0054       0.041        0.78         260

  S2 -- Misfeeding              45         1.2x10^-5^        0.0062       0.044        1.0          260

  S3 -- Render reduction factor 48         9.6x10^-6^        0.0055       0.041        0.83         260

  S4 -- Poultry litter          45         1.6x10^-5^        0.0060       0.044        0.92         260

  S5 -- Canadian BSE Prevalence 260        0.20              2.1          60           260          770

  S6 -- All assumptions         290        0.26              3.1          120          270          840
  --------------------------------------------------------------------------------------------------------------

The base case results (see Tables 6a, 6b, and Section 1 of Appendices 2A
and 2B) indicate that over a 20-year period, imports of cattle from
Canada are expected to produce a total of 21 BSE-infected cattle in the
U.S. (5^th^ percentile = 12 cases and 95^th^ percentile = 30 cases). The
simulation predicts that the vast majority of these cases will be
imported, while approximately 10% (2.1 cases expected over 20 years,
5^th^ percentile = 0 cases, 95^th^ percentile = 6 cases) will represent
secondary cases resulting from exposure of cattle in the U.S. to BSE
from the imported cattle. The relatively small number of predicted
native U.S. cases reflects the relatively small estimated R~0~ value
(mean of 0.044, 5^th^ percentile = 0, 95^th^ percentile = 0.25).
Population potential human exposure to the BSE agent is expected to
total 45 cattle oral ID~50~s over 20 years.

The sensitivity analysis results indicate that the assumed Canadian BSE
prevalence rate is by far the most important source of uncertainty. Use
of the pessimistic value for this assumption increases the expected
total number of BSE cases from 21 to 120, and the expected number of
secondary BSE cases from 2.1 to 12. Total predicted potential human
exposure increases from an expected value of 45 cattle oral ID~50~s over
20 years to 260 cattle oral ID~50~s over that time period.

Among the other uncertain assumptions analyzed, the misfeeding rate and
the proportion of poultry litter used in cattle feed are most
influential, although neither of these is nearly as influential as the
assumed prevalence of BSE in Canada. Simultaneous use of pessimistic
values for all five uncertain assumptions described here results in a
total of 150 BSE cases in the U.S., 42 of which are native to this
country. Mean human exposure to the BSE agent increases to 290 cattle
oral ID~50~s over 20 years.

The R~0~ parameter is an important aggregate measure of the U.S.
agricultural system's robustness in the face of potential disease
release scenarios because it indicates whether BSE prevalence will tend
to grow or whether BSE will die out over time. The average R~0~ value
for the base case analysis was 0.044. More importantly, the 95^th^
percentile value for this parameter was 0.25, indicating that if the
base case assumptions are valid, it is very unlikely that the disease's
prevalence will grow over time independently of the import of additional
infectivity. That is, it is very unlikely that R~0~ \> 1. Even
simultaneous use of pessimistic values for all five assumptions
evaluated here yielded the prediction that R~0~ \> 1 is unlikely.
Overall, these results indicate that any plausible release of BSE into
the U.S. results in only a limited spread of the disease among cattle in
this country. Equivalently, the results indicate that in the absence of
a continual release of BSE into the U.S., its prevalence will decrease
over time, eventually leading to its elimination. From this prediction,
it follows that potential human exposure to the BSE agent would be
limited.

# References {#references .JTC-Heading-1}

# Appendix 1 Base Case Parameter File Changes From Earlier Analysis {#appendix-1-base-case-parameter-file-changes-from-earlier-analysis .JTC-Heading-1}

# Appendix 2 Detailed Simulation Output {#appendix-2-detailed-simulation-output .JTC-Heading-1}

# Appendix 3 Numerical Stability of Simulation Output {#appendix-3-numerical-stability-of-simulation-output .JTC-Heading-1}

[^1]: While on the research staff at the Harvard Center for Risk
    Analysis, the author of this report was a lead developer of the
    Harvard BSE simulation model. He has been involved in several risk
    assessments conducted using that software. Dr. Cohen moved to the
    Tufts New England Medical Center in January, 2006.

[^2]: For this analysis, tissue from non-ambulatory animals cannot be
    used in human food.

[^3]: Compliance program implementation details can be found at
    http://www.fda.gov/cvm/CVM_Updates/BSE0305.htm.

[^4]: According to FDA, "An OAI inspection classification occurs when
    significant objectionable conditions or practices were found and
    regulatory sanctions are warranted in order to address the
    establishment\'s lack of compliance with the regulation. An example
    of an OAI inspection classification would be findings of
    manufacturing procedures insufficient to ensure that ruminant feed
    is not contaminated with prohibited material. Inspections classified
    with OAI violations will be promptly re-inspected following the
    regulatory sanctions to determine whether adequate corrective
    actions have been implemented" (8).

    "A VAI inspection classification occurs when objectionable
    conditions or practices were found that do not meet the threshold of
    regulatory significance, but do warrant advisory actions to inform
    the establishment of findings that should be voluntarily corrected.
    Inspections classified with VAI violations are more technical
    violations of the Ruminant Feed Ban. These include provisions such
    as minor recordkeeping lapses and conditions involving non-ruminant
    feeds" (8).

[^5]: Discussed in Cohen et al. (1) Appendix 1 at 2.18.3