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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

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

Probability of Illness0.0

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

Cum

ulative Percentile (%)

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

Mean R

isk per Serving - Target A

LOP

, log10

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

Resulting Plant PO for Target ALOP

Obj

ecti

ve F

unct

ion

16Plant PO, log10 cfu/g

-10 -8 -6 -4 -2 0 2Probability that risk per serving <= target ALOP (%

)

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 2Probability that risk per serving <= target ALOP (%

)

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 10.420 -4.57 -6.45 20.830 -4.19 -6.44 30.340 -3.70 -6.43 41.350 -3.06 -6.42 50.060 -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 2Probability that risk per serving <= target ALOP (%

)

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

Cum

ulative Percentage (%)

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

Cum

ulative Percentage (%)

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

Cum

ulative Percentage (%)

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

Probability Risk per Serving <= target ALO

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

Probability Risk per Serving <= target ALO

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

Probability Risk per Serving <= target ALO

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?