Data UtilityData Utility : : Improvements by Targeting Study Improvements by Targeting Study

Design and Sampling PlansDesign and Sampling PlansPeg ColemanPeg Coleman

Senior MicrobiologistSenior Microbiologist

Syracuse Research CorporationSyracuse Research Corporation

RAC CharterRAC Charter Through the Risk Assessment Consortium, the Through the Risk Assessment Consortium, the

agencies will collectively work to enhance agencies will collectively work to enhance communication and coordination between federal communication and coordination between federal agencies and promote the conduct of scientific agencies and promote the conduct of scientific research that will facilitate risk assessments. research that will facilitate risk assessments.

Such research will assist the regulatory agencies Such research will assist the regulatory agencies in fulfilling their specific food-safety risk in fulfilling their specific food-safety risk management mandates. management mandates.

Goals of the Risk Assessment Consortium:Goals of the Risk Assessment Consortium:• Reduce uncertainties inherent in risk assessment by Reduce uncertainties inherent in risk assessment by

identifying data gaps and critical research needsidentifying data gaps and critical research needs

•Improve risk assessment Improve risk assessment researchresearch

RAC Data Utility Work GroupRAC Data Utility Work Group

Characteristic of a project or Characteristic of a project or assessment with defined scope and assessment with defined scope and explicit applications or decisionsexplicit applications or decisions

Exercise of multifactorial judgments Exercise of multifactorial judgments for inclusion and exclusion of studiesfor inclusion and exclusion of studies

Inherent in process of accounting for Inherent in process of accounting for data quality, variability and uncertaintydata quality, variability and uncertainty

Characteristic of a project or assessmentCharacteristic of a project or assessment

Generation of targeted multidisciplinary research Generation of targeted multidisciplinary research to address data gaps and research needs for to address data gaps and research needs for microbial risk analysis requires current awareness microbial risk analysis requires current awareness of priority needs of risk assessors within research of priority needs of risk assessors within research communitycommunity

Examples of Needs to address bias and interpret Examples of Needs to address bias and interpret confounding effects of extrapolationsconfounding effects of extrapolations

• Predictive MicrobiologyPredictive Microbiology• Dose ResponseDose Response

Characteristic of a project or assessmentCharacteristic of a project or assessment

Examples of Updating ExtrapolationsExamples of Updating Extrapolations• Predictive Microbiology: need to address bias and Predictive Microbiology: need to address bias and

interpret confounding effectsinterpret confounding effects

Stochastic nature of bacterial growth in non-homogeneous Stochastic nature of bacterial growth in non-homogeneous compartmentalized shell egg (SERA, FSIS, 2005 revision compartmentalized shell egg (SERA, FSIS, 2005 revision 11))

Jameson effect in meat, poultry, and fish products (ECRA, Jameson effect in meat, poultry, and fish products (ECRA, FSIS, 2005 revision FSIS, 2005 revision 22))

Effects of initial dose and culture conditions (ECRA, FSIS, Effects of initial dose and culture conditions (ECRA, FSIS, 2005 revision 2005 revision 33))

1 Marks & Coleman, in press; Marks & Coleman, in press; 2 Coleman Sandburg, Anderson, 2003; Coleman Sandburg, Anderson, 2003; 3 Coleman, Tamplin, Phillips, Marmer 2003Coleman, Tamplin, Phillips, Marmer 2003

Experimental Design for Risk AssessmentExperimental Design for Risk AssessmentARS/FSIS/UMES Predictive Microbiology StudyARS/FSIS/UMES Predictive Microbiology Study

2 x 2 x 3 factorial design2 x 2 x 3 factorial design agitation: shaken vs unshakenagitation: shaken vs unshaken initial density: high vs lowinitial density: high vs low incubation temperatures 37, 19, 10°Cincubation temperatures 37, 19, 10°C

Protocol HighlightsProtocol Highlights Brain Heart Infusion brothBrain Heart Infusion broth pH 5.5 (typical of ground beef)pH 5.5 (typical of ground beef) duplicate flasks per treatment using duplicate flasks per treatment using

staggered inoculum (Oscar, 1998)staggered inoculum (Oscar, 1998) triplicate experiments using one straintriplicate experiments using one strain

O157:H7 Growth (37 C, pH 5.5)

0

2

4

6

8

10

12

0 2 4 6 8 10 12 14 16

time (hours)

log

CFU

/mL

Shaken, high N(0)

Shaken, high N(0)

Unshaken, low N(0)

Unshaken, low N(0)

-1

1

3

5

7

9

11

0 50 100 150 200 250 300time (hours)

log

CF

U/m

L

high N(0)

low N(0)

Effect of Initial Density 10 C

Characteristic of a project or assessmentCharacteristic of a project or assessment Examples for Future ExtrapolationsExamples for Future Extrapolations

• Dose-Response Needs to address bias and Dose-Response Needs to address bias and interpret confounding effectsinterpret confounding effects

Strain variability for campylobacteriosis and Strain variability for campylobacteriosis and salmonellosis (FSIS feasibility study salmonellosis (FSIS feasibility study 1,2,3))

Cellular immunity for campylobacteriosis (RAC Dose-Cellular immunity for campylobacteriosis (RAC Dose-Response Work Group: seminar at Naval Medical Response Work Group: seminar at Naval Medical Research Center, 2003)Research Center, 2003)

Mucosal immunity (host variability) and strain Mucosal immunity (host variability) and strain variability for cryptosporidiosis (RAC public meeting & variability for cryptosporidiosis (RAC public meeting & ongoing projects at EPA/OW ongoing projects at EPA/OW 44))

1 Coleman & Marks, 1998; Coleman & Marks, 1998; 2 Coleman & Marks, 2000; Coleman & Marks, 2000; 3 Coleman, Marks, Coleman, Marks, Golden, Latimer, 2004; Golden, Latimer, 2004; 44 Teunis, Chappell, Okhuysen, Teunis, Chappell, Okhuysen, 2000a,b; 2000a,b; 55 Coleman, Tamplin, Phillips, 2003 Coleman, Tamplin, Phillips, 2003

Some Multifactorial Judgments (Ideal Data Utility)Some Multifactorial Judgments (Ideal Data Utility)

RepresentativenessRepresentativeness• large dataset from reproducible controlled clinical large dataset from reproducible controlled clinical

trial, experimental study, or probabilistic survey trial, experimental study, or probabilistic survey representative of populations of interest with study representative of populations of interest with study design that accounts for major biological and/or design that accounts for major biological and/or social confounding factors (regional, seasonal, social confounding factors (regional, seasonal, demographic variability, …)demographic variability, …)

RelevanceRelevance• Pertinent or predictive of situation of interestPertinent or predictive of situation of interest

RobustnessRobustness• Consistent performance for multiple investigatorsConsistent performance for multiple investigators

Some Multifactorial Judgments (Ideal Data Utility)Some Multifactorial Judgments (Ideal Data Utility)

Generalizability or external validityGeneralizability or external validity• Predictions consistent with other data Predictions consistent with other data

sources and studiessources and studies Soundness of study conclusions or Soundness of study conclusions or

internal validityinternal validity• Acceptable adequacy, completeness, and Acceptable adequacy, completeness, and

soundness of conclusionssoundness of conclusions DefensibilityDefensibility

• Use consistent with body of scientific Use consistent with body of scientific evidence from multiple sourcesevidence from multiple sources

accounting for data quality, variability, accounting for data quality, variability, uncertaintyuncertainty

Utility involves both getting the right Utility involves both getting the right science and getting the science rightscience and getting the science right

Examples for Examples for E. coliE. coli O157:H7 O157:H7• Jameson Effect for Predictive Jameson Effect for Predictive

Microbiology in Food MatricesMicrobiology in Food Matrices• Human and animal clinical studies for Human and animal clinical studies for

dose-responsedose-response

Figure 2: Initial densities of three pathogens, Campylobacter jejuni/coli, Listeria monocytogenes, and Salmonella spp.and Aerobic Plate Counts as a surrogate for the spoilage microflora at adjusted geometric mean densities per 100 mL

Spoilage Flora

EC O157:H7

inhibition

8x10 3

ND

Salmonella <1

Listeria 3

Numerical dominance of spoilage flora of ground beef (FSIS baseline survey, n=543, CFU/g in 25-g samples)

Ecological AdvantageEcological Advantage

PseudomonasPseudomonas

Rate at 2°CRate at 2°C 0.09/hr 0.09/hr

Rate at 4°CRate at 4°C 0.11/hr 0.11/hr

Rate at 10 °CRate at 10 °C 0.24/hr 0.24/hr

E. Coli E. Coli O157:H7O157:H7

Below T Below T MinimumMinimum

Below T Below T MinimumMinimum

0.028 0.028 unshaken,lowN(0)unshaken,lowN(0)

0.037 0.037 unshaken,highN(0)unshaken,highN(0)

Model Predictions for Baseline ScenarioModel Predictions for Baseline Scenario(Coleman, Sandburg, Anderson, 2003)(Coleman, Sandburg, Anderson, 2003)

PseudomonasPseudomonas9595thth percentile percentile

2x102x1099 /100-g serving /100-g serving 5050thth percentile percentile

6x106x1066 /100-g serving /100-g serving 55thth percentile percentile

6x106x1044 /100-g serving /100-g serving

MaximumMaximum 4x104x101515 /100-g serving /100-g serving

E. Coli E. Coli O157:H7O157:H7

00

00

00

3x103x1033 /100-g serving /100-g serving

Questions to be addressed for Questions to be addressed for O157 Dose-Response ModelingO157 Dose-Response Modeling

Appropriate Surrogates?Appropriate Surrogates? Plausible Model Forms?Plausible Model Forms?

• Should dose-response models have Should dose-response models have specific terms for species, strain, host, specific terms for species, strain, host, and matrix effects and interactions?and matrix effects and interactions?

• How do we estimate variability and How do we estimate variability and uncertainty?uncertainty?

Low-dose extrapolation?Low-dose extrapolation?

Infant Rabbit Dose-ResponseInfant Rabbit Dose-Response

0.00

0.20

0.40

0.60

0.80

1.00

1.E+02 1.E+04 1.E+06 1.E+08 1.E+10

log(dose)

Fre

qu

ency

data

Predicted

Weibull-Gamma Fit for O157:H7

Infant Rabbit Clinical Study Infant Rabbit Clinical Study Pai et al., 1986Pai et al., 1986

Dose Frequency n1.E+05 0.3 31.E+06 0.4 51.E+07 1.0 51.E+08 0.9 131.E+09 1.0 53.E+09 1.0 21.E+10 1.0 6

Sampling and Design Issues for Sampling and Design Issues for Exposure AssessmentExposure Assessment

Heterogeneous distributions in foodsHeterogeneous distributions in foods• If growth, then clustersIf growth, then clusters

Non-monotonic distributionsNon-monotonic distributions• SE eggsSE eggs

Need for not only prevalence (+/-) but Need for not only prevalence (+/-) but levelslevels

Small, convenience sampling designs Small, convenience sampling designs unlikely to be sufficiently representative for unlikely to be sufficiently representative for maximum utility to risk assessorsmaximum utility to risk assessors

Sampling and Design Issues for Sampling and Design Issues for Dose-ResponseDose-Response

Defining curves for dose-response Defining curves for dose-response relationships may require higher targets relationships may require higher targets (~10 dose groups), multiple aspects of (~10 dose groups), multiple aspects of disease triangle disease triangle (host, pathogen, matrix)(host, pathogen, matrix)

Include low-dose regionInclude low-dose region

Develop more mechanistic approaches using Develop more mechanistic approaches using in vitro,in vitro, human, and animal data, established human, and animal data, established dosimetry practices, immunological and dosimetry practices, immunological and physiological data for inter-species physiological data for inter-species extrapolations extrapolations (RAC Dose Response Work Group)(RAC Dose Response Work Group)

AcknowledgmentsAcknowledgments

Risk Assessment ConsortiumRisk Assessment Consortium USDA Food Safety & Inspection USDA Food Safety & Inspection

Service, Agricultural Research Service, Agricultural Research Service Service

University of Maryland Eastern University of Maryland Eastern ShoreShore

Syracuse Research Corporation (SRC)Syracuse Research Corporation (SRC)Microbial Risk Assessment Center of Microbial Risk Assessment Center of

Excellence (MRACE) Points of Contact:Excellence (MRACE) Points of Contact:

Dave L. ColangeloDave L. ColangeloDirector, Government AffairsDirector, Government Affairscolangelo@syrres.comcolangelo@syrres.com

Pat McGinnisPat McGinnis Associate Director, SRC Environmental Science Center Associate Director, SRC Environmental Science Center mcginnis@syrres.commcginnis@syrres.com

Peg ColemanPeg ColemanSenior Microbiologist Senior Microbiologist mcoleman@syrres.commcoleman@syrres.com