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An Outcomes Model to Evaluate Risks and Benefitsof Escherichia coli Vaccination in Beef Cattle
H. Scott Hurd1 and Sasidhar Malladi2
Abstract
We developed a stochastic simulation model to evaluate the impact of Escherichia coli O157:H7 (O157) vacci-nation on key epidemiological outcomes. The model evaluated a reduction in the O157 prevalence in feedlotcattle as well as concentration in cattle feces due to vaccination. The impact of this reduction on outcomes atslaughter/harvest and consumption was evaluated by simulating the relationships between the O157 prevalenceand concentration at various points in the ground beef supply chain. The uncertainty and variability associatedwith the O157 contamination was explicitly modeled in production, slaughter, and consumption modules. Ourresults show that vaccination can have a significant benefit with respect to relevant outcomes such as (1) thenumber of human O157 illnesses due to the consumption of ground beef, (2) the number of production lots withhigh O157 contamination levels, (3) the likelihood of detection by U.S. Department of Agriculture Food Safetyand Inspection Service testing, and (4) the probability of multiple illnesses due to ground beef servings from thesame lot. These results show that these outcomes are strongly impacted by preharvest vaccination. For example,if the vaccine is used so as to reduce the prevalence of E. coli shedding cattle by 80% and if all U.S. steers andheifers were vaccinated, the expected number of human illnesses from ground beef-associated O157 would bereduced almost 60%. If the vaccine is 60% or 40% effective, the illness rate would be reduced approximately 45%or 40%, respectively. The number of production lots (10,000-lb lots) with high O157 contamination levels (>1000servings) would be reduced by 96% if all steers and heifers received an 80% effective vaccine regimen. Theanalysis shows that resulting reduction in the number of shedding animals and the reduced concentration ofE. coli on carcasses can combine to reduce human illnesses and cost to beef packers.
Introduction
Approximately 265,000 of the estimated 48 millionfoodborne illness cases each year are caused by Shiga
toxigenic Esherichia coli (STEC), with E. coli serogroupO157:H7 (O157) responsible for 36% and non-O157 ser-ogroups for the remainder (CDC, 2011). Symptoms of STECinfections include severe stomach cramps, bloody diarrhea,and vomiting. If fever develops, it rarely exceeds 101�F(38.5�C). Most people recover within 5–7 days, but some de-velop severe or life-threatening complications, including he-molytic uremic syndrome. Young children, the elderly, andpeople who are immunocompromised face higher risk fromSTEC infections than healthy adults.
For beef cattle producers and the meat industry, O157contamination creates significant economic burden, legal lia-bility, and public health concern. Ground beef that tests pos-itive for O157 is considered adulterated, so even a low
prevalence of contaminated meat produces a major economicrisk for packers. Publicity surrounding recalls has alsoheightened awareness about bacterial contamination amongconsumers, with 40% saying they are extremely concerned(NCBA, 2010). In practice, reducing O157 contamination re-quires vigilance along the entire supply chain from farm tofork. Currently, postharvest processes, such as low wateractivity, chilled storage, and carcass wash procedures are wellestablished and on average work well. For example, the na-tional ground beef prevalence of O157 is about 0.2% (USDA-FSIS, 2009). Yet occasionally the high prevalence of O157 incattle at the production stage aligns with high O157 carcasspresence at the harvest stage, producing high O157 concen-tration at the consumption stage. The convergence of theseoutlier events on a single day (an event day) can produceground beef production lots with an exceptionally high O157concentration in the final product. Some say a single eventday, with its extra testing requirements, quality control
1Department of Production Animal Medicine, College of Veterinary Medicine, and World Health Organization Collaborating Center forRisk Analysis and Hazard Surveillance and Intervention in Food Animals, Iowa State University, Ames, Iowa.
2College of Veterinary Medicine, Center for Animal Health and Food Safety, University of Minnesota, St. Paul, Minnesota.
FOODBORNE PATHOGENS AND DISEASEVolume 9, Number 10, 2012ª Mary Ann Liebert, Inc.DOI: 10.1089/fpd.2012.1150
952
interventions, and internal and/or external recalls, can exact asignificant economic toll.
Recently, two O157-specific bacterial extract vaccines foruse in feedlot cattle have been granted conditional approvalby the U.S. Department of Agriculture (USDA). The vaccinesdo not entirely prevent infections, but preliminary datademonstrated that vaccination reduced the percentage ofanimals shedding O157 at slaughter (Thomson et al., 2009;Thornton et al., 2009). This phenomenon could potentiallydecrease the prevalence of contaminated carcasses. The valueof preharvest O157 vaccination hinges on three questions:Will vaccination significantly reduce the number of humanillnesses and other relevant outcomes resulting from beefcontamination with O157? Will those reductions offset thecost of vaccination relative to other interventions? And willthe system (farm-to-fork) provide a sufficient signal to cattlefeeders to vaccinate? The objective of this article is to providequantitative analysis of the effect vaccination may have onhuman health and food safety.
Materials and Methods
Model development
We developed a stochastic simulation model that explicitlyevaluates the impact of vaccination in reducing O157 preva-lence and concentration in cattle feces. The model includesproduction, slaughter, and consumption modules, designed toexamine four relevant outcomes: (1) the number of humanO157 illnesses due to the consumption of ground beef, (2) thenumber of production lots with high O157 contaminationlevels, (3) the likelihood of detection by USDA Food Safety andInspection Service (FSIS) testing, and (4) the probability ofmultiple illnesses due to ground beef servings from the samelot. We then modeled the impact of preharvest O157 vacci-nation on these outcomes. Due to space constraints, this articlewill discuss only the results for outcomes 1 and 2.
Our stochastic model was developed to simulate the O157prevalence and concentration at various points in the groundbeef supply chain starting with feedlots and continuing
through various production steps, as the basis for predictingthe number of O157 illnesses due to consumption of groundbeef from U.S. steers and heifers and imported ground beef.The exact cattle prevalence and other variables are imperfectlyknown and constantly changing. Limited available studiesgive a range and statistical distributions that are often notnormal (Poisson, Gamma); therefore, the model used thesedistributions of possible numbers to calculate multiple results.Key modeling assumptions appear in Table 1.
The parameters and calculations of the three modules arelisted in Table 2. The Production Module estimates relevantparameters at the feedlot level, such as O157 prevalenceamong feedlots and among cattle within O157-positive feed-lots, under different vaccination scenarios. Given the limitedpublished data on O157 vaccine efficacy, there is significantuncertainty for the values of the parameters (P10 and E) re-presenting the impact of O157 vaccination on the prevalenceand concentration in feces. Therefore, in this article, wemodeled these parameters with three scenarios.
The simulation model was based on the approximatedlinear relationships between O157 prevalence in feces and oncarcasses (Barkocy-Gallagher et al., 2003; Arthur et al., 2004;Elder et al., 2000) (Fig. 1). We fit a linear regression model withintercept equal to zero using data from the three studies(R2 = 0.68). Uncertainty associated with this parameter wasestimated by resampling from the data using @RISK and re-calculating the linear coefficient. The final distribution for S1had a mean of 2 (90% prediction interval [PI], 0.71–4.34). Asimilar linear model has been used by others and underpinsthe 2001 FSIS ground beef risk assessment. Similarly, linearmodels were also utilized to simulate the decreases in O157concentration due to vaccination or generic slaughter levelinterventions (Cassin et al., 1998).
The Slaughter Module estimates the O157 prevalence andconcentration on beef carcasses and in ground beef compo-nents at various processing points. The specific process pointsmodeled include (1) on carcasses preevisceration, (2) on car-casses after generic postevisceration interventions and chil-ling, (3) in production lots of trim, and (4) in production lots of
Table 1. Key Modeling Assumptions
1. All ground beef imported into the United States is destined for mixing with domestic ground beef in portions driven byexternal economic factors.
2. The ground beef processed in the United States is domestically consumed, i.e., no exports.3. The Escherichia coli O157:H7 prevalence and concentration in imported ground beef is similar to that in ground beef from
non-vaccinated domestic steers and heifers.4. Neither the mixing of ground beef from cows and bulls with the ground beef from steers and heifers, nor vaccination of
cows and bull was considered in the current model.5. The O157:H7 prevalence in a load of preevisceration beef carcasses is linearly proportional to the fecal prevalence in the
feedlot from which the cattle are derived.6. The risk reduction effect of test-and-hold protocols implemented by the beef industry is not evaluated in this version of
the simulation model.7. The number (colony forming units [CFU]/serving or CFU/325-g sample) of O157:H7 in a portion of ground beef from a
production lot is Poisson distributed.8. The number (CFU/60 pieces of trim) of O157:H7 in an N60 sample from a production lot is Poisson distributed.9. The simulation model was developed for a yearly time frame, and seasonal variations in parameters were not considered.
We assume that parameter estimates represent yearly averages.10. The annual number of O157:H7 cases due to consumption of ground beef was calculated from the observed human
illnesses in the baseline year reported in the Centers for Disease Control and Prevention FoodNet data combined with theaccepted but flawed attribution parameter of the human illnesses to ground beef consumption.
11. The current study focuses on the O157:H7 strain. We do not consider other Shiga toxin–producing Escherichia coli strainsin this study.
OUTCOMES MODEL OF O157:H7 VACCINATION IN BEEF CATTLE 953
Ta
bl
e2.
Su
mm
ar
yo
fK
ey
Pa
ra
me
te
rs
an
dT
he
ir
Dist
rib
ut
io
nU
se
din
Mo
de
lo
fE
sc
he
ric
hia
co
li
017:
H7
Ou
tc
om
es
An
al
ysis
Par
amet
erd
escr
ipti
onD
ata
sou
rces
For
mu
la/d
istr
ibu
tion
Est
imat
edv
alu
e
Pro
du
ctio
nm
odu
leT
he
mea
nn
um
ber
of
catt
lep
erfe
edlo
t(P
1)N
AH
MS
99D
eter
min
isti
cp
aram
eter
5500
Nu
mb
ero
fst
eers
and
hei
fers
slau
gh
tere
dp
ery
ear
(P2)
US
DA
—26
,000
,000
(in
bas
elin
ey
ear)
Am
on
gfe
edlo
tp
rev
alen
ceo
fE
.co
liO
157:
H7
(P4)
Sar
gea
nt
etal
.,20
03B
eta(
71,4
)95
%(9
0–98
%)a
Mea
np
rev
alen
ceo
fE
.co
liO
157:
H7
ina
po
siti
ve
feed
lot
(P5)
Sm
ith
etal
.,20
01;
Wo
rner
etal
.,20
06;
Sar
gea
nt
etal
.,20
03
Bet
a(19
14,
2132
2)/
P4
14.2
%(1
3.5–
15%
)a
Vac
cin
eef
fect
iven
ess
inre
du
cin
gE
.co
liO
157:
H7
fece
sp
rev
alen
cein
afe
edlo
t(P
10)
Inp
ut
par
amet
er—
Set
at80
%,
60%
,an
d40
%fo
rth
isan
aly
sis
Sla
ug
hte
rm
odu
leR
atio
of
pre
evis
cera
tio
nca
rcas
sp
rev
alen
ceto
feca
lp
rev
alen
cein
catt
lefr
om
the
feed
lot
(S1)
Eld
ers,
2000
;B
ark
ocy
-G
alla
gh
er,
2003
;A
rth
ur,
2007
Cu
sto
mst
och
asti
cd
istr
ibu
tio
nfr
om
sim
ula
tio
nre
sult
s(F
ig.
1)2
(0.7
1–4.
34)
Eff
ecti
ven
ess
of
gen
eric
po
stev
isce
rati
on
inte
rven
tio
ns
inre
du
cin
gca
rcas
sp
rev
alen
ceo
fE
.co
liO
157:
H7
(S2)
Eld
ers
etal
.,20
00;A
rth
ur
etal
.,20
04;
Bar
ko
cyG
alla
gh
eret
al.,
2003
Bet
a(53
7,22
)96
%(9
4–97
%)
Ind
icat
or
for
wh
eth
erth
eu
nv
acci
nat
edfe
edlo
tis
con
tam
inat
edw
ith
E.
coli
O15
7:H
7(S
3u)
—B
ino
mia
l(1
,P4)
95%
a
Th
ep
rev
alen
ceo
fE
.co
liO
157:
H7
inca
ttle
fro
man
un
vac
cin
ated
feed
lot
(S4
u)
—M
in(1
,Ex
po
nen
tial
(P5)
)14
.2%
(0.7
–42.
5%)
Est
imat
edp
rev
alen
ceo
fE
.co
liO
157:
H7
amo
ng
pre
evis
cera
tio
nb
eef
carc
asse
sfr
om
anu
nv
acci
nat
edfe
edlo
t(S
5u)
—(1
-(1
-m
in(S
1*
S4
u,1
))2
37.6
%(2
–100
%)
Nu
mb
ero
fst
eers
and
hei
fers
con
trib
uti
ng
trim
toa
10,0
00-l
bp
rod
uct
ion
lot
of
gro
un
db
eef
trim
(S6)
—10
,000
/(c
arca
ssw
eig
ht
*p
erce
nt
trim
)68
Nu
mb
ero
fE
.co
liO
157:
H7–
po
siti
ve
carc
asse
saf
ter
gen
eric
po
stev
isce
rati
on
inte
rven
tio
nfr
om
anu
nv
acci
nat
edfe
edlo
tco
ntr
ibu
tin
gtr
imfo
rth
ep
rod
uct
ion
lot
(S7u
)
—B
ino
mia
l(S
6,
(S5
u)
*(1
-S
2))
0.96
7(0
–4)
Ind
icat
or
for
wh
eth
erth
efe
edlo
tw
ith
vac
cin
ated
catt
leis
con
tam
inat
edw
ith
E.
coli
O15
7:H
7(S
8v)
—B
ino
mia
l(1
,P4)
95%
a
Th
ep
rev
alen
ceo
fE
.co
liO
157:
H7
inca
ttle
fro
ma
vac
cin
ated
feed
lot
(S9
v)
Var
ies
wit
hv
acci
ne
effe
ctiv
enes
s(i
np
ut
par
amet
er)
Bin
om
ial
(P1
*S
4u,
(1-
P1
0))
/P
1)
Dep
end
so
nin
pu
tsc
enar
io
Est
imat
edp
rev
alen
ceo
fE
.co
liO
157:
H7
amo
ng
pre
evis
cera
tio
nb
eef
carc
asse
sfr
om
anv
acci
nat
edfe
edlo
t(S
10
v)
—1
-(1
-m
in(S
1*
S9
v,1
))2
37.6
%(2
–100
%)
Th
esu
rfac
ear
eaco
nta
min
ated
wit
hE
.co
liO
157:
H7
per
con
tam
inat
edca
rcas
s(S
11)
Ass
um
pti
on
and
sam
pli
ng
surf
ace
area
(Art
hu
r,20
04;
Bri
chta
-Har
hay
,20
07)
Ap
pro
xim
ate
val
ue,
may
be
adju
sted
toca
lib
rate
mo
del
sim
ilar
to20
01F
SIS
gro
un
db
eef
RA
8000
cm2/
carc
ass
(con
tin
ued
)
954
Ta
bl
e2.
(Co
nt
in
ue
d)
Par
amet
erd
escr
ipti
onD
ata
sou
rces
For
mu
la/d
istr
ibu
tion
Est
imat
edv
alu
e
Sla
ug
hte
rm
odu
leA
ver
age
carc
ass
wei
gh
t(S
12)
Inp
ut
par
amet
erE
stim
ated
fro
mN
AS
S20
09d
ata
814
lb/
carc
ass
Per
cen
to
fd
ress
edca
rcas
sw
eig
ht
pro
cess
edin
tog
rou
nd
bee
ftr
imp
erst
eer
or
hei
fer
(S1
3)
Ass
um
pti
on
—18
%
Th
eE
.co
liO
157:
H7
aver
age
surf
ace
con
cen
trat
ion
on
ap
reev
isce
rati
on
carc
ass
of
un
vac
cin
ated
catt
le(S
14
u,k
)A
rth
ur,
2004
;B
rich
ta-H
arh
ay,
2007
;B
ark
ocy
Gal
lag
her
,20
03
Em
pir
ical
dis
cret
ed
istr
ibu
tio
n.
Mea
n,
7(0
–6)
CF
U/
100
cm2
Th
esu
rfac
ear
eaco
nta
min
ated
wit
hE
.co
liO
157:
H7
per
con
tam
inat
edca
rcas
s(S
15
,k)
Ass
um
pti
on
and
sam
pli
ng
surf
ace
area
(Art
hu
r,20
04;
Bri
chta
-Har
hay
,20
07)
Ap
pro
xim
ate
val
ue,
may
be
adju
sted
toca
lib
rate
mo
del
sim
ilar
to20
01F
SIS
gro
un
db
eef
RA
8000
cm2/
carc
ass
To
tal
CF
Uo
fE
.co
liO
157:
H7
on
aca
rcas
sat
pre
evic
erat
ion
step
(CF
U/
carc
ass)
S1
6,k
—S
14
u*
S1
5,k
/10
0M
ean
,56
5C
FU
/ca
rcas
s
Lo
gre
du
ctio
no
fE
.col
iO
157:
H7
con
cen
trat
ion
du
eto
gen
eric
po
stev
isce
rati
on
trea
tmen
ts(S
17
,k)
Art
hu
ret
al.,
2004
,20
01;
FS
ISri
skas
sess
men
t(W
illi
ams
etal
.,20
10)
Lo
gn
orm
ald
istr
ibu
tio
nb
ou
nd
edat
(0,
3.5)
1.06
(0.2
9–2.
32)
To
tal
CF
Uo
fE
.co
liO
157:
H7
on
aco
nta
min
ated
carc
ass
afte
rg
ener
icp
ost
evis
cera
tio
ntr
eatm
ent
(C
FU
/ca
rcas
s)(S
18
,k)
—S 1
6,k=1
0S 2
7,k
Mea
n,
93
CF
Uo
fE
.co
li01
57:H
7ad
ded
toa
10,0
00-l
bp
rod
uct
ion
lot
of
trim
fro
ma
con
tam
inat
edca
rcas
sfr
om
un
vac
cin
ated
feed
lot
(S1
9u
,k)
75%
of
carc
ass
surf
ace
area
assu
med
tog
oto
war
ds
gro
un
db
eef
trim
0.75
*S
18
,k82
(0–6
2)m
ax*
30,0
00
Am
ou
nt
of
E.
coli
0157
:H7
ina
10,0
00-l
bp
rod
uct
ion
lot
fro
mu
nv
acci
nat
edca
ttle
(CF
U/
pro
du
ctio
nlo
t)(S
21
u)
—+S 7
u
k¼
0
S 19
u,k
63(0
–77)
max
*30
,000
Av
erag
eco
nce
ntr
atio
no
fE
.co
liO
157:
H7
ina
10,0
00-l
bp
rod
uct
ion
lot
fro
mu
nv
acci
nat
edca
ttle
per
gra
m(S
22
u)
—S
21
u/
(10,
000
*45
4)1.
42(0
–2)
*10
-5
CF
U/
g
Est
imat
edp
rev
alen
ceo
fE
.co
liO
157:
H7
in32
5-g
gro
un
db
eef
po
rtio
ns
ina
pro
du
ctio
nlo
tfr
om
un
vac
cin
ated
catt
le(S
23
u)
—1�
e�
S 22
u�3
25
0.35
%(0
–0.5
%)
Est
imat
edp
rev
alen
ceo
fE
.co
liO
157:
H7
inse
rvin
gs
fro
ma
pro
du
ctio
nlo
tfr
om
un
vac
cin
ated
catt
le(
S2
4u)
—1�
e�
S2
2u�8
70.
115%
(0–0
.15%
)
Ex
pec
ted
nu
mb
ero
fco
nta
min
ated
serv
ing
sp
erp
rod
uct
ion
lot
fro
mu
nv
acci
nat
edca
ttle
(S2
5u)
—(1
0,0
00�
45
4
87
)S24
u60
(0–7
5)m
ax*
15,0
00
Per
cen
tag
eo
fd
om
esti
cg
rou
nd
bee
fin
ap
rod
uct
ion
lot
(S2
6)
Use
rin
pu
t59
%in
bas
elin
esc
enar
ioN
um
ber
of
E.
coli
O15
7:H
7–p
osi
tiv
eca
rcas
ses
fro
ma
vac
cin
ated
feed
lot
ina
pro
du
ctio
nlo
tin
wh
ich
the
do
mes
tic
gro
un
db
eef
trim
isfr
om
vac
cin
ated
catt
le(S
27
v)
—B
ino
mia
l(S
6*
S2
6,
(S1
0v)
*(1
-S
2))
Dep
end
so
nin
pu
tsc
enar
io
Nu
mb
ero
fE
.co
liO
157:
H7–
po
siti
ve
carc
asse
sfr
om
anu
nv
acci
nat
ed(i
mp
ort
ed)
sou
rces
ina
pro
du
ctio
nlo
tin
wh
ich
the
do
mes
tic
gro
un
db
eef
trim
isfr
om
vac
cin
ated
catt
le(S
28)
—B
ino
mia
l(S
6*
(1-
S2
6),
(S5
u)
*(1
-S
2))
Dep
end
so
nin
pu
tsc
enar
io
To
tal
amo
un
to
fE
.co
li01
57:H
7in
a10
,000
-lb
pro
du
ctio
nlo
tfr
om
vac
cin
ated
catt
le(C
FU
/p
rod
uct
ion
lot)
S2
9v
—S 2
9v¼
+S 27
v
k¼
1
S 20
v,kþ
+S 28
l¼1
S 19
u,l
Dep
end
so
nin
pu
tsc
enar
io
(con
tin
ued
)
955
Ta
bl
e2.
(Co
nt
in
ue
d)
Par
amet
erd
escr
ipti
onD
ata
sou
rces
For
mu
la/d
istr
ibu
tion
Est
imat
edv
alu
e
Sla
ug
hte
rm
odu
leA
ver
age
con
cen
trat
ion
of
E.
coli
O15
7:H
7in
a10
,000
-lb
pro
du
ctio
nlo
tfr
om
vac
cin
ated
catt
lep
erg
ram
(S3
0v)
CF
U/
g
—S
29
v/
(10,
000
*45
4)D
epen
ds
on
inp
ut
scen
ario
Est
imat
edp
rev
alen
ceo
fE
.co
liO
157:
H7
in32
5-g
gro
un
db
eef
po
rtio
ns
ina
pro
du
ctio
nlo
tfr
om
vac
cin
ated
catt
le(S
31
v)
—1�
e�
S 30
v�3
25
Dep
end
so
nin
pu
tsc
enar
io
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imat
edp
rev
alen
ceo
fE
.co
liO
157:
H7
inse
rvin
gs
fro
ma
pro
du
ctio
nlo
tfr
om
vac
cin
ated
catt
le(
S3
2v)
—1�
e�
S2
9v�8
7D
epen
ds
on
inp
ut
scen
ario
Ex
pec
ted
nu
mb
ero
fco
nta
min
ated
serv
ing
sp
erp
rod
uct
ion
lot
fro
mv
acci
nat
edca
ttle
(S3
3v)
—(1
0,0
00�
45
4
87
)S32
vD
epen
ds
on
inp
ut
scen
ario
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ean
nu
aln
um
ber
of
gro
un
db
eef
pro
du
ctio
nlo
tsfr
om
vac
cin
ated
catt
lep
roce
ssed
inth
eU
nit
edS
tate
s(S
34
v)
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4v¼
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S 12�
S 13
10
00
0�
S 26
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end
so
nin
pu
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enar
io
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ean
nu
aln
um
ber
of
gro
un
db
eef
pro
du
ctio
nlo
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om
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cin
ated
catt
lep
roce
ssed
inth
eU
nit
edS
tate
s(S
35
u)
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S 13
10
00
0�
S 26
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end
so
nin
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io
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icat
or
for
wh
eth
erth
ep
rod
uct
ion
lot
isv
acci
nat
ed(S
36)
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36
=B
inom
ial(
1,f)
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end
so
nin
pu
tsc
enar
io
Th
en
um
ber
of
con
tam
inat
edse
rvin
gs
fro
ma
pro
du
ctio
nlo
tfr
om
asl
aug
hte
rp
lan
tw
her
ef
afr
acti
on
of
pro
du
ctio
nlo
tsis
fro
mv
acci
nat
edca
ttle
(S3
7)
—S 3
7¼
S 33
vif
S 36¼
1
S 25
uif
S 36¼
0
��
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end
so
nin
pu
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enar
io
Ind
icat
or
for
wh
eth
erth
en
um
ber
of
con
tam
inat
edse
rvin
gs
fro
mth
ep
rod
uct
ion
lot
isg
reat
erth
an10
00("
ho
tlo
t")
(S3
8)
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8¼
1if
S 37q
10
00
0if
S 37<
10
00
��
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end
so
nin
pu
tsc
enar
io
Pre
val
ence
of
E.
coli
0157
:H7–
po
siti
ve
325-
gsa
mp
les
fro
ma
pro
du
ctio
nlo
td
epen
din
go
nw
het
her
itis
vac
cin
ated
(S3
9).
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bab
ilit
yo
fd
etec
tio
nb
yF
SIS
per
vis
itb
ased
on
gro
un
db
eef.
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9¼
S 31
vif
S 36¼
1
S 23
uif
S 36¼
0
��
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end
so
nin
pu
tsc
enar
io
An
nu
alp
rob
abil
ity
of
det
ecti
ng
E.
coli
0157
:H7
via
FS
ISM
T43
rou
tin
eg
rou
nd
bee
fte
stin
gp
roje
ct(S
40)
—S
40
=1
-(E
(1-
S3
9))
24
Dep
end
so
nin
pu
tsc
enar
io
Pre
val
ence
of
E.
coli
0157
:H7
intr
imsa
mp
lefr
om
ap
rod
uct
ion
lot
bas
edo
nN
60te
stin
gp
roto
col
(S4
1)
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(1�
e�
S 31
v�A
n6
0A
pl)
ifS 3
6¼
1
(1�
e�
S 22�A
n60
Apl
)if
S 36¼
0
8 < :9 = ;
Dep
end
so
nin
pu
tsc
enar
io
S4
2:
An
nu
alp
rob
abil
ity
of
det
ecti
ng
E.
coli
0157
:H7
via
rou
tin
eN
60g
rou
nd
bee
ftr
imte
stin
g—
S4
2=
1-
(E(1
-S
41))
3D
epen
ds
on
inp
ut
scen
ario
S4
3:
An
nu
alp
rob
abil
ity
of
det
ecti
ng
E.
coli
0157
:H7
via
rou
tin
eF
SIS
gro
un
db
eef
or
trim
test
ing
pro
gra
ms
—S
43
=1
-(1
-S
42)
(1-
S4
0)
Dep
end
so
nin
pu
tsc
enar
io
S4
4:
Nu
mb
ero
fE
.co
liO
157:
H7
illn
esse
sca
use
db
yg
rou
nd
bee
ffr
om
ap
rod
uct
ion
lot.
—S
44*
Bin
om
ial
(S3
7,
C8)
Dep
end
so
nin
pu
tsc
enar
io
S4
5:
Ind
icat
or
var
iab
lefo
rm
ult
iple
illn
esse
sd
ue
toco
nsu
mp
tio
no
fg
rou
nd
bee
ffr
om
the
sam
ep
rod
uct
ion
lot
—S 4
5¼
1if
S 45q
M
0if
S 45<
M
��
Dep
end
so
nin
pu
tsc
enar
io
(con
tin
ued
)
956
Ta
bl
e2.
(Co
nt
in
ue
d)
Par
amet
erd
escr
ipti
onD
ata
sou
rces
For
mu
la/d
istr
ibu
tion
Est
imat
edv
alu
e
Con
sum
pti
onm
odu
leA
ver
age
serv
ing
size
for
gro
un
db
eef
ing
ram
s(C
1)
2001
FS
ISg
rou
nd
bee
fri
skas
sess
men
t.C
SF
IIsu
rvey
dat
a
Det
erm
inis
tic
par
amet
er87
g
Fra
ctio
no
fE
.co
li01
57:H
7ca
ses
cau
sed
by
the
con
sum
pti
on
of
gro
un
db
eef
(eti
olo
gic
frac
tio
nC
2)
Wit
hee
,20
09;
Ran
gel
,20
05;
Kas
sen
bo
rg,
2004
,20
01;
FS
ISri
skas
sess
men
t
Cu
sto
md
iscr
ete
dis
trib
uti
on
26%
(15–
37%
)
Nu
mb
ero
fg
rou
nd
bee
fse
rvin
gs
con
sum
edp
ery
ear
inth
eU
nit
edS
tate
sin
bas
elin
ep
ery
ear
(C3)
—E
stim
ated
amo
un
to
fg
rou
nd
bee
fp
roce
ssed
inth
eU
nit
edS
tate
s/C
1
4692
7*
106
serv
ing
s
Bas
elin
ep
rev
alen
ceo
fE
.co
li01
57:H
7in
325-
gg
rou
nd
bee
fsa
mp
les
bas
edo
nF
SIS
test
ing
dat
a(C
4)
FS
ISg
rou
nd
bee
fro
uti
ne
ver
ifica
tio
nd
ata
for
yea
r20
09
Bet
a(36
,115
39)
0.31
%(0
.23–
0.40
%)
Bas
elin
ep
rev
alen
ceo
fE
.co
li01
57:H
7in
87-g
gro
un
db
eef
serv
ing
sb
ased
on
FS
ISte
stin
gd
ata
(C5)
—C
5¼
1�
e�
ln(1�
C4
)
32
5
�� �87
0.08
%(0
.06–
0.10
%)
Bas
elin
en
um
ber
of
E.
coli
O15
7:H
7co
nta
min
ated
gro
un
db
eef
serv
ing
sfr
om
do
mes
tic
pro
du
ctio
nb
ased
on
FS
ISsa
mp
lin
g(C
6)
—C
5*
C3
39(2
9–50
)m
illi
on
serv
ing
s
An
nu
alex
pec
ted
nu
mb
ero
fd
om
esti
call
yac
qu
ired
E.
coli
O15
7:H
7il
lnes
ses
inth
eU
nit
edS
tate
s(C
7)
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llan
etal
.,20
11C
7¼
(Foo
dnet
case
s)�
(und
erdi
agno
sis
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tipl
ier)=
(per
cent
ofU
Spo
pula
tion
info
odne
tar
ea)
76,5
00
Bas
elin
ep
rob
abil
ity
of
E.
coli
O15
7:H
7il
lnes
sp
erco
nta
min
ated
serv
ing
of
gro
un
db
eef
con
sum
ed(C
8)
—C
8¼
C7�
C2
C6
5.2
(2.6
–8.1
)*
10-
4il
lnes
sp
erse
rvin
gM
ean
nu
mb
ero
fE
.co
liO
157:
H7
con
tam
inat
edse
rvin
gs
fro
mu
nv
acci
nat
edp
rod
uct
ion
lots
per
yea
r(C
11
u)
—C
11
u=
E(S
35
u*
S2
5u)
Dep
end
so
nin
pu
tsc
enar
io
Mea
nn
um
ber
of
E.
coli
O15
7:H
7co
nta
min
ated
serv
ing
sfr
om
vac
cin
ated
pro
du
ctio
nlo
tsp
ery
ear
(C1
4v)
—C
14
v=
E(S
34
v*
S3
3v)
Dep
end
so
nin
pu
tsc
enar
io
Mea
nn
um
ber
of
E.
coli
O15
7:H
7il
lnes
ses
cau
sed
du
eto
con
sum
pti
on
of
gro
un
db
eef
fro
mfe
edlo
tca
ttle
and
imp
ort
edg
rou
nd
bee
f(C
15)
—C
15
=E
(C1
1+
C1
4v)
Dep
end
so
nin
pu
tsc
enar
io
aT
wo
-sid
ed90
%p
rob
abil
ity
inte
rval
.T
he
sub
scri
pts
uan
dv
den
ote
par
amet
ers
rela
ted
tou
nv
acci
nat
edca
ttle
and
vac
cin
ated
catt
lere
spec
tiv
ely
.T
he
sym
bo
lE
den
ote
sex
pec
ted
val
ue
of
ap
aram
eter
.P
ois
son
dis
trib
uti
on
isa
sin
gle
par
amet
erd
iscr
ete
pro
bab
ilit
yd
istr
ibu
tio
nth
atis
use
dto
rep
rese
nt
the
nu
mb
ero
fev
ents
occ
urr
ing
inan
inte
rval
of
tim
eo
rsp
ace.
Giv
enth
eP
ois
son
dis
trib
uti
on
,th
eex
pec
ted
nu
mb
ero
fev
ents
inan
inte
rval
isp
rop
ort
ion
alto
the
size
of
the
inte
rval
.B
eta
dis
trib
uti
on
isa
two
-par
amet
erco
nti
nu
ou
sd
istr
ibu
tio
no
ften
use
dto
mo
del
the
un
cert
ain
tyas
soci
ated
wit
hth
ep
rob
abil
ity
of
anev
ent.
Th
eb
eta(
S+
1,N
-S+
1)d
istr
ibu
tio
nis
use
din
Bay
esia
nan
aly
sis
tom
od
elth
eu
nce
rtai
nty
inth
ep
rob
abil
ity
of
succ
ess
of
aB
ino
mia
lp
roce
ssw
hen
ssu
cces
ses
are
ob
serv
edin
Ntr
ials
.B
ino
mia
ld
istr
ibu
tio
nis
the
dis
cret
ep
rob
abil
ity
dis
trib
uti
on
of
the
nu
mb
ero
fsu
cces
ses
ina
seq
uen
ceo
fn
ind
epen
den
tex
per
imen
tsw
ith
two
po
ssib
leo
utc
om
esin
each
exp
erim
ent,
each
of
wh
ich
yie
lds
succ
ess
wit
hp
rob
abil
ity
p.
NA
HM
S,
Nat
ion
alA
nim
alH
ealt
hM
on
ito
rin
gS
yst
em;
US
DA
,U
.S.
Dep
artm
ent
of
Ag
ricu
ltu
re;
FS
IS,
US
DA
Fo
od
Saf
ety
and
Insp
ecti
on
Ser
vic
e;C
FU
,co
lon
y-f
orm
ing
un
its.
957
ground beef. The statistical relationships utilized in thismodule were derived from data linking the O157 prevalenceand concentration at the processing level to the correspondingvariables at the feedlot level. The final output of this module isthe O157 prevalence in servings of ground beef from 10,000-lbproduction lots. The variability in the O157 concentration andprevalence in production lots of trim and raw ground beefinfluence critical outcomes for packers.
Data on O157 contamination in slaughter plants also indi-cate that there is a high degree of variance in the O157 con-centration in a production lot. Consequently, a small fractionof production lots may be contaminated to a high degree(‘‘hot’’ lots), although the average load per production lot isrelatively small. We defined a ‘‘hot’’ lot as one containingmore than 1,000 contaminated servings of ground beef. Thisvariability is likely derived from the variance in prevalenceand concentration of O157 observed in feedlot cattle (fecessamples) and beef carcasses, as well as the presence of some‘‘super shedders’’ in herds ( Jacob et al., 2010; Gyles, 2007;Barkocy-Gallagher et al., 2003). We assumed that testingmethods were sensitive enough to detect a single colony-forming unit (CFU) of O157 in a 325-g portion of ground beef,as recommended by the FSIS guidelines (Murphy and Sew-ard, 2004).
The Consumption Module estimates the number of O157human illnesses attributed to consumption of ground beeffrom domestic steer and heifer slaughter and importedground beef (Table 2). The mixing of imported ground beefwith ground beef from domestic steer and heifer slaughterwas considered (Bosilevac et al., 2006). We estimated thatabout 40% of the ‘‘average’’ production lot consisted of im-ported ground beef based upon ground beef imports and totalground beef production from steer and heifer slaughter in theUnited States.
The first section of the Consumption Module estimates abaseline probability of an O157 illness per contaminatedserving of ground beef (C8). In the second section, theSlaughter Module outputs of the number of O157-contaminated servings of ground beef in a production lot (S25u
and S33v) and the annual number of production lots fromvaccinated and unvaccinated cattle (S34v and S35u) are utilized
to estimate the number of O157 illnesses in the current year inwhich a specific percent of fed cattle have been vaccinated.
To estimate the baseline probability that an O157-contaminated ground beef serving results in an illness, westarted with the fraction of O157 illnesses caused due to con-sumption of ground beef (the etiologic fraction), from outbreakdata and epidemiological studies. The annual number of O157cases due to consumption of ground beef was then calculatedbased on CDC Foodnet surveillance data for 2009, the baselineyear, and the etiologic fraction. The baseline probability that anO157-contaminated ground beef serving results in a humanillness was calculated as the ratio of the number of O157 ill-nesses caused by consumption of ground beef and the esti-mated number of contaminated ground beef servings.
Scenarios for the impact of O157 vaccination
We analyzed three scenarios for the impact of O157 vacci-nation. In Scenarios A, B, and C, we assumed vaccine inducedprevalence reductions were 80%, 60%, and 40%. Additionally,for scenarios A, B, and C we assumed reduction of 1, 0.3, and0.3 log10 CFU/carcass reduction in average concentration onresulting carcasses, respectively. To estimate the impact ofvaccination under these scenarios, the simulation model wasrun for 100,000 iterations using @RISK with Monte CarloSampling.
Results
Reduction in human illnesses due to vaccination
The first outcome evaluated was the value of vaccinationwith respect to the overall number of human O157 casesprevented. Figure 2 and Table 3 show the relationship be-tween the predicted number of human cases of O157 and thenumber of feedlot cattle vaccinated (termed ‘‘productionfunction’’ in Withee et al., 2009). The model demonstrated thatthe number of human illnesses attributed to consumption ofO157-contaminated ground beef can be greatly reduced byvaccination. If the vaccine were used so as to be 80% effective,and if all U.S. steers and heifers were vaccinated, the expectednumber of human illnesses could be reduced by almost 60%
FIG. 1. Linear estimation of Escherichia coli O157:H7 prevalence in cattle feces and preevisceration carcasses (%) based onpublications shown.
958 HURD AND MALLADI
(90% PI, 56–60). Even with partial adoption of vaccination insay 50% of feedlots (80% effective), human illnesses would bereduced by nearly 30% (90% PI, 26–32), from approximately20,057 to 14,231 annual cases on average (Fig. 2). In scenario C,with a 40% effective vaccine, human illnesses would be re-duced from approximately 20,057 to 12,187 annual cases withfull adoption. These results suggest that, depending on vac-cine performance parameters, 2,000–3,300 cattle must bevaccinated to prevent one O157 illness. Note that a portion ofthe illnesses in these scenarios are associated with importedground beef and would not be prevented with vaccination ofdomestic cattle.
Decrease in contaminated ground beef production lotsdue to vaccination
Table 4 shows the impact of vaccination, as adoption in-creases, on the annual number of ‘‘hot’’ production lots. A‘‘hot-lot’’ was defined as one containing more than 1,000contaminated servings in a 10,000-lb lot, as calculated for ahypothetical slaughter plant producing 16,000 lots per year.Hot lots or event days may result in an internal recall by thepacker. The extra work required to recover, rework, cook, ordestroy product is costly. All levels of vaccine efficacy andadoption reduced risk to the packer. Full adoption of an 80%
effective vaccine (Scenario A) could reduce the chance of hotlots by 96%.
Discussion
Our results demonstrate that preharvest vaccinationagainst O157 could have a significant benefit for the beef in-dustry by reducing the likelihood of hot lots or event days,and the probability of multiple illnesses due to contaminatedground beef servings. In addition, the model results indicatethat a large number of ground-beef-associated human O157illnesses may be prevented by vaccinating domestic feedlotcattle. Thus, both the packer and the consumer could benefitfrom routine O157 vaccination of cattle.
In our study, the relationship between the number of hu-man illnesses and vaccinated cattle was quite linear (R2 = 0.99),similar to the model of Withee et al. (2009). By comparison, wealso modeled a reduction in concentration on/in carcasses/feces as well as prevalence of shedders due to vaccination (Fig.1). Incorporating a reduction in concentration produced anadditional impact on epidemiological outcomes.
We propose the reason that vaccine is so effective is itsimpact on outlier events and the tail-ends of the non-normaldistributions. Some have speculated about the possibility of‘‘super-shedder’’ cattle ( Jacob et al., 2010; Gyles, 2007). Most
FIG. 2. Change in the mean number of human illnesses due to Escherichia coli O157:H7 with the number of cattle vaccinated.Scenario A: Vaccine 80% effective, also 1 log10 CFU/carcass reduction in average concentration on resulting carcass. ScenarioB: Vaccine 60% effective, also 0.3 log10 CFU/carcass reduction in average concentration on resulting carcass. Scenario C:Vaccine 40% effective, also in 0.3 log10 CFU/carcass reduction in average concentration on resulting carcass.
Table 3. Predicted Annual Escherichia coli O157:H7 Illnesses per Year Due to Consumption of Ground Beef
from Steer and Heifer Slaughter with the Percentage of U.S. Cattle Vaccinated (Adoption Rate)
Mean number of illnesses
Vaccine adoption rate Scenario A Scenario B Scenario C
0% 20057 (10182–30500) 20057 (10182–30500) 20057 (10182–30500)40% 15396 (7800–23500) 16486 (8398–25252) 16909 (8635–25957)80% 10736 (5400–16300) 12916 (6617–19890) 13761 (7088–21303)100% 8405 (4268–12830) 11130 (5731–17211) 12187 (6321–18982)
The intervals shown here are two-sided 95% probability intervals that represent the impact of uncertainty in input parameter as well asvariability in the ground beef processing steps. Possible reasons for the relatively wide intervals include the significant uncertainty in theetiologic fraction for O157 illnesses due to ground beef consumption and the significant variability in the number of contaminated servingsper production lot (e.g., due to high shedders).
OUTCOMES MODEL OF O157:H7 VACCINATION IN BEEF CATTLE 959
packers agree the occurrence of ‘‘event days’’ is sporadic.Figure 3 shows the tail of the histogram (segment of the his-togram showing highly contaminated production lots) ofsimulation results for the prevalence of O157 in 325-g samplesby comparing vaccinated and unvaccinated production lots.The x-axis is the prevalence of positive samples expressed as afraction, and the y-axis is the relative frequency (proxy forprobability from simulation results). The graph shows that theprobabilities of a very highly contaminated production lot canbe reduced considerably with vaccination. This is becausevaccination is an independent mitigation measure frompostharvest interventions. Vaccination seems to impact thefew rare cases where the production lot could be highlycontaminated due to various outlier events.
Given that a substantial portion of ground beef consumedin the United States is imported and to aid in further economicmodeling, we modeled ground beef imports and the mixing ofdomestic and imported ground beef explicitly. This approachis conservative (reducing the observed impact of vaccination)since a smaller proportion of the O157 illnesses are attributedto the consumption of domestic ground beef, reducing theestimates of human illnesses which can be prevented byvaccinating domestic feedlot cattle. Withee et al. (2009) did notconsider ground beef imports and attributed a greater num-
ber of human illnesses to the consumption of ground beeffrom domestic cattle slaughter. Unlike Hurd et al. (2010), weassumed a constant probability that contaminated servings ofground beef cause illness, regardless of the specific productstreams (e.g., food service) or consumption channel (e.g., re-tail sale). The differences in risks for various product streamsand consumption channels were not considered here, as thefocus of the current study was on the impact of vaccination onoverall human O157 illnesses due to consumption of all ca-tegories of ground beef. Given that only approximately 3% ofall raw ground beef is sold directly to the consumer, this is anarea for further research (NCBA, 2004).
Significant uncertainty surrounds several input variablesassociated with slaughter processes, such as the amount of acarcass surface area represented in ground beef trim or theeffectiveness of carcass decontamination treatments. Giventhese uncertainties, the model results for slaughter outcomesare more appropriate for predicting the relative impact ofO157 vaccination, rather than for predicting the absolutelevels of these outcomes. The results for slaughter outcomemeasures are representative of average values for a hypo-thetical large production plant in the United States and are notapplicable for any specific slaughter establishment. However,we believe that the modeling approach utilized here would beappropriate for evaluating outcomes for specific slaughterplants, provided that the input parameters are calibrated ac-cording to the establishment characteristics.
There is significant uncertainty about the inputs in thismodel representing the impact of O157 vaccination. Pre-liminary data indicated that vaccination reduces the O157prevalence and concentration in feces ( p £ 0.05 based onsampling at specific times post vaccination as describedelsewhere [Thomson et al., 2009; Thornton et al., 2009]).
We modeled these parameters as inputs that users can varyto produce different scenarios to consider the impact of theassociated uncertainty. Depending on the nature of the bio-logical processes associated with the functioning of the vac-cine, it is possible that the reduction in prevalence is correlatedwith other variables, such as the reduction in fecal
Table 4. Change in Predicted Annual Number
of ‘‘Hot’’ Production Lots ( > 1,000 Contaminated
Servings) for Slaughter Plants Producing 16,000 Lots
per Year with the Percent of Cattle Vaccinated
Vaccineadoption rate Scenario A Scenario B Scenario C
0% 144 (124–163) 144 (124–163) 144 (124–163)40% 82 (67–97) 105 (88–122) 115 (98–133)80% 30 (21–39) 73 (60–88) 90 (75–106)100% 6 (2–10) 57 (45–69) 77 (63–91)
The numbers in the parentheses are two-sided 95% probabilityintervals.
FIG. 3. Tail-end of histogram showing impact of vaccination on number of production lots with high Escherichia coli O157prevalence ( > 5%) in 325-g samples. Scenario B: Vaccine 60% effective, 100% adoption.
960 HURD AND MALLADI
concentration. For example, a large reduction in O157 fecesconcentration due to vaccination may also be associated witha correspondingly large reduction in O157-measured fecalprevalence. Further studies are required to examine the po-tential correlation between the reductions in concentrationand reductions in prevalence under different vaccine dosageregimens and field conditions. Such studies will also providethe data required to evaluate the impact of vaccination sto-chastically, that is with correlated probability distributions forreduction in feces concentrations and reduction in prevalence.
This stochastic model based on production, slaughter, andconsumption factors demonstrated that preharvest O157vaccination could reduce human illnesses and decrease con-taminated ground beef lots. The analysis shows that vaccine-associated reduction in the number of shedding animals andthe reduced concentration of O157 in feces both combine toreduce human illnesses. Thus, the benefits of preharvest O157vaccination of cattle extend to packers as well as consumers.
Acknowledgments
Dr. Guy Loneragan from Texas Tech University assisted ininterpreting data from vaccine trials. Sarah Staples, M.A.,E.L.S., assisted with manuscript preparation.
Disclosure Statement
This project was funded by an unrestricted grant to IowaState University from Pfizer Animal Health (New York, NY).Manuscript preparation was supported by an unrestrictedgrant from Pfizer Animal Health.
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Address correspondence to:H. Scott Hurd, D.V.M., Ph.D.
Department of Production Animal MedicineCollege of Veterinary Medicine
Iowa State UniversityAmes, IA 50011
E-mail: [email protected]
OUTCOMES MODEL OF O157:H7 VACCINATION IN BEEF CATTLE 961
