incorporating risk metrics into food safety regulations: l. monocytogenes in ready-to-eat deli meats...
TRANSCRIPT
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Using Quantitative Risk Assessment and Accounting for Variability and Uncertainty
Incorporating risk metrics into food safety regulations: L. monocytogenes in ready-to-eat deli meats
Daniel GallagherVirginia Tech
12th Annual Joint Fera/JIFSAN Symposium Greenbelt, MDJune 15-17, 2011
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Traditional Regulatory Controls Examples
Poultry cooked to minimum of 165°F Milk pasteurized at 72°C for 15 sec Food code safety criteria Aw < 0.95 & pH < 5.5 L. monocytogenes zero tolerance
(sampling: < 1 cfu / 25 g)
Major components in HACCP plans Critical control points
Not directly related to public health / illness rate Inflexible, not conductive to innovation
Adapted from Buchanan & Whiting, CFSAN/FDA 2004
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New Risk Based MetricsVocabulary
Appropriate Level of Protection (ALOP) The level of protection deemed appropriate to protect health
Food Safety Objective (FSO) The maximum frequency and/or concentration of a hazard in a
food at the time of consumption that provides or contributes to the appropriate level of protection (ALOP)
Performance Objective (PO) The maximum frequency and/or concentration of a hazard in a
food at a specified step in the food chain before the time of consumption that provides or contributes to a FSO or ALOP, as applicable
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Application of RM Metricsto a Food Process
Application of RM Metricsto a Food Process
Time
PathogenLevel
Enter slaughter Point ofconsumption
ALOPPO
Current risk
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Different approaches
Traditional approach incorporates variability and uncertainty at each step of the process. The resulting estimated number of illnesses is an uncertain distribution.
The risk metric approach considers the number of illnesses as a fixed goal.
This research: incorporate uncertainty and variability into the performance objective (PO) at the plant, i.e. the PO is an uncertainty distribution.
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ALOP as fixed goal
Brief overview: risk metrics
Food processing plant
Retail grocery store
Consumption in the home
Listeriosis illnesses
ALOPRisk per serving
FSOdose
PORegulated
concentration
PORegulated
concentration
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Implementation
Written in R 2.13 snow package for parallel processing
Latin Hypercube design for selecting uncertainty realizations
Each run: 240 uncertainty simulations each with 7.5 million variability realizations
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Major Data Sources
Plant Lm concentration distribution: FSIS reporting
Growth, lag times, plant-to-retail transport: Pradhan et al. 2009 Transport times/temperature, lag times, growth rates (with &
without growth inhibitors)
Retail Cross contamination : Endrikat et al. 2010 Simplified z-score approach
Consumer handling: Pouillot et al. 2010 storage time / temperature varies by retail vs plant sliced
Dose-response: WHO/FAO
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Baseline Conditions
Uncertainty Starting plant
concentration distribution▪ Log10 normal
distribution▪ mean: -9.22, SD: 2.92▪ correlation: -0.99
Fraction of product with growth inhibitor (50-60%)
Variability Growth rates Lag times Storage times /
temperatures Serving sizes
Nonstochastic Dose response r
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WHO / FAO Dose Response
Dose, log10 cfu
6 8 10 12 14 16
Pro
ba
bility
of Illn
es
s0.0
0.2
0.4
0.6
0.8
1.0Healthy, medianSusceptible, median
rDeill 1)Pr(
Baseline: r fixedrhealthy = 2.41e-14rsusceptible = 1.05e-12
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-15.0 -14.0 -13.0 -12.0
0.70
0.75
0.80
0.85
0.90
Risk per serving, log10
Cum
ulat
ive
Pro
babi
lity
Variability and Uncertainty2nd order Monte Carlo
variability run for given uncertainty realization
-15.0 -14.0 -13.0 -12.0
0.70
0.75
0.80
0.85
0.90
Risk per serving, log10
Cum
ulat
ive
Pro
babi
lity
Multiple variability runs for different uncertainty realizations
Uncertainty distribution of given statistic of each variability run
13Mean risk of illness per serving, log10
-6.50 -6.45 -6.40 -6.35 -6.30 -6.25
Cu
mu
lativ
e P
erc
en
tile (%
)
0
20
40
60
80
100
Baseline current industry risk per serving distribution
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Example Result
Results of 1 uncertainty run.N = 1e5ALOP = -6.5Truncated industry response
PO
Max growth level
1:1 lineno growth
Cross contamination
Retail sliced without growth inhibitor
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Root finding for Plant POEach uncertainty run
Plant PO, log10 cfu/g
-30 -20 -10 0 10
Me
an R
isk pe
r Se
rving - T
arg
et A
LO
P, lo
g1
0
-1.4
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
Resulting Plant PO for Target ALOP
Ob
jecti
ve F
un
cti
on
16Plant PO, log10 cfu/g
-10 -8 -6 -4 -2 0 2Pro
ba
bility
tha
t risk
pe
r se
rvin
g <
= ta
rge
t AL
OP
(%)
0
20
40
60
80
100
-6.33 (Q95)-6.36 (Q75)-6.38 (Q50)-6.41 (Q25)-6.45 (Q5)-6.50
Plant PO, log10 cfu/g
-10 -8 -6 -4 -2 0 2Pro
ba
bility
tha
t risk
pe
r se
rvin
g <
= ta
rge
t AL
OP
(%)
0
20
40
60
80
100
-6.33 (Q95)-6.36 (Q75)-6.38 (Q50)-6.41 (Q25)-6.45 (Q5)-6.50
Plant PO ResultsTruncated Industry
Target ALOP
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Deconvolution VerificationTruncated industry response. Target ALOP of log10 risk per serving = -6.416 (Q25 of the ALOP distribution).
PO Quantile (%) Plant POMean Risk
per Serving, log10
Fraction of Risk per Serving
Distribution > Target ALOP (%)
10 -4.98 -6.46 10.4
20 -4.57 -6.45 20.8
30 -4.19 -6.44 30.3
40 -3.70 -6.43 41.3
50 -3.06 -6.42 50.0
60 -2.34 -6.41 60.4
Based on a target ALOP and industry response, an uncertainty distribution for the PO was calculated. Different quantiles of this PO distribution were then set as the regulatory PO and the resulting uncertainty distribution of risk per serving generated.
18Plant Lm Distribution
For a fixed ALOP, different industry response assumptions lead to different POs
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Plant PO results by industry response Target ALOP = -6.38 (Q50)
Plant PO, log10 cfu/g
-10 -8 -6 -4 -2 0 2Pro
ba
bility
tha
t risk
pe
r se
rvin
g <
= ta
rge
t AL
OP
(%)
0
20
40
60
80
100
truncatedshiftedfixed
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Industry Risk per ServingDifferent Uncertainty Assumptions
Mean Risk of Illness per Serving, log10
-7.5 -7.0 -6.5 -6.0 -5.5 -5.0
Cu
mu
lativ
e P
erc
en
tag
e (%
)
0
20
40
60
80
100
industry, baselineindustry, dose response uncertaintyindustry, increased GI uncertainty
Mean Risk of Illness per Serving, log10
-7.5 -7.0 -6.5 -6.0 -5.5 -5.0
Cu
mu
lativ
e P
erc
en
tag
e (%
)
0
20
40
60
80
100
industry, baselineindustry, dose response uncertaintyindustry, increased GI uncertainty
Mean Risk of Illness per Serving, log10
-7.5 -7.0 -6.5 -6.0 -5.5 -5.0
Cu
mu
lativ
e P
erc
en
tag
e (%
)
0
20
40
60
80
100
industry, baselineindustry, dose response uncertaintyindustry, increased GI uncertainty
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Incorporating Dose-Response UncertaintyTruncated industry response
Plant PO, log10 cfu/g
-35 -30 -25 -20 -15 -10 -5 0
Pro
ba
bility
Ris
k p
er S
erv
ing
<=
targ
et A
LO
P (%
)
0
20
40
60
80
100
Baseline, target ALOP = -6.41 (Q25 of baseline)DR uncertainty, target ALOP = -6.41DR uncertainty, target ALOP = -6.66 (Q25 industry with DR uncertain)
Plant PO, log10 cfu/g
-35 -30 -25 -20 -15 -10 -5 0
Pro
ba
bility
Ris
k p
er S
erv
ing
<=
targ
et A
LO
P (%
)
0
20
40
60
80
100
Baseline, target ALOP = -6.41 (Q25 of baseline)DR uncertainty, target ALOP = -6.41DR uncertainty, target ALOP = -6.66 (Q25 industry with DR uncertain)
Plant PO, log10 cfu/g
-35 -30 -25 -20 -15 -10 -5 0
Pro
ba
bility
Ris
k p
er S
erv
ing
<=
targ
et A
LO
P (%
)
0
20
40
60
80
100
Baseline, target ALOP = -6.41 (Q25 of baseline)DR uncertainty, target ALOP = -6.41DR uncertainty, target ALOP = -6.66 (Q25 industry with DR uncertain)
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Conclusions
Incorporating uncertainty into risk metrics is technically feasible computationally intensive much greater technical demands on risk
managers with uncertainty, adapting PO to actual
regulations difficult▪ industry-wide compliance, not individual food plant▪ need to monitor for entire distribution▪ extremely broad PO uncertainty distributions
In practice, current levels of uncertainties limit applicability for L. monocytogenes
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Acknowledgements
Funding: FSIS Project AG-3A94-P-08-0148
Co authors at FSIS and Virginia Tech Eric Ebel, Owen Gallagher, David
LaBarre, Michael Williams, Neal Golden, Janell Kause, Kerry Dearfield
Régis Pouillot for assistance with dose-response modeling.
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Questions?