Interagency Risk Assessment:

Listeria monocytogenes in

Retail Delicatessens

_____ 

Technical Report

The Interagency Retail Listeria monocytogenes 

Risk Assessment Workgroup 

September 2013
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

i 

Interagency Retail Listeria monocytogenes Risk Assessment Workgroup

(In alphabetical order) 

DARE AKINGBADE1 

NATHAN BAUER1 

SHERRI DENNIS2

DANIEL GALLAGHER3 

KARIN HOELZER2,4

JANELL KAUSE1

RÉGISPOUILLOT2 

MERYL SILVERMAN1,5

JIA TANG3

1 Food Safety and Inspection Service, U.S. Department of Agriculture, Washington, DC 20250

2 Center for Food Safety and Applied Nutrition, Food and Drug Administration, College Park, MD 20740

3 Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061 

4 formerly with the Department of Food Science, Cornell University, Ithaca, NY 14850

5 formerly with the University of Maryland, Joint Institute for Food Safety and Applied Nutrition, College Park, MD 20740
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

ii

Acknowledgments

The Interagency Retail Listeria monocytogenes Workgroup thanks the many contributions made by others 

in the U.S. Department of Agriculture, Food Safety and Inspection Service (FSIS); the U.S. Food and 

Drug Administration (FDA); and the Centers for Disease Control and Prevention (CDC). We are also 

grateful for coordination of an independent peer review by Versar, Inc.; focus studies conducted by 

members of academia, to fulfill specific data needs in this risk assessment; and input from both consumer 

groups and industry throughout this risk assessment. Specific recognition includes the following. 

We acknowledge and thank FSIS scientists and risk managers for their input and support throughout the 

development of this risk assessment: Kristina Barlow, Phil Bronstein, Vivian Chen, Phil Derfler, Denise 

Eblen, Daniel Engeljohn, David Goldman, Steve Mamber, Evelyne Mbandi, Tim Mohr, and William 

Shaw. We acknowledge and thank the members of the FDA Risk Management team, including Elizabeth 

Calvey, Ted Elkin, Faye Feldstein (retired), Mary Losikoff, Mickey Parish, Jenny Scott, Kevin Smith, 

Mark Wirtz, and Donald Zink, for advice throughout the process. We also thank our colleagues from the 

CDC for their valuable input: Ezra Barzilay, Patricia Griffin, Martha Iwamoto, K. Liane Ong, Benjamin 

Silk, and Robert Tauxe. 

We are grateful to the following individuals for their contributions to the success of the public meeting 

that initiated this risk assessment and that actively included stakeholder and public input: Frederick 

Angulo1

, Daphne Brown2, John Butts3, Greg DiNapoli2, Frances Ann Draughon4, Amanda Eamich2, 

Linda Eckrich2

, Rachel Edelstein2, Atiya Khan2, Ndidi Mojay2, Amirhossein Mokhtari5, Laura Reiser2, 

Peggy Riek2

, and Robert Tynan2. We thank the many individuals from industry and consumer groups who

provided useful input in the development and refinement of this risk assessment: Susan L. Backus6

, Betsy 

 

1 Centers for Disease Control and Prevention

2 Food Safety and Inspection Services, U.S. Department of Agriculture

3 Land O’Frost

4 University of Tennessee 5 Research Triangle Institute (RTI) International

6 American Meat Institute Foundation
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

iii

Booren6, Pat Buck7, Caitlin Catella8, Tony Corbo9, Joe Corby10, Nancy Donley11, Jill Hollingsworth12, 

Susan Kendrick13, Sara Klein8

, Larry Kohl12, Felicia Nestor9, Tanya Roberts7, Caroline Smith DeWaal8, 

Warren Stone14, Kimberly Stryker15, Hilary Thesmar12, and Carol Tucker-Forman16. 

We thank the following individuals for their roles in studies involving retail deli employee behavior, 

longitudinal study/risk mapping of L. monocytogenes in the retail grocery deli environment, and 

transmission of L. monocytogenes in retail delis: Shirley Bohm17, Renee Boyer18, Laura Brown2

, 

Elizabeth Calvey19, Benjamin Chapman20, Philip G. Crandall21, Susan Duncan18, Joseph Eiffert18, Caren 

Kieswetter17, Brenda Le1

, Jessica Maitland18, Jianghong Meng19, Haley Oliver22, Carol Selman1, Courtney 

Simmons23, Martin Wiedmann23, and Denita Williams1

. We also thank Vangelia Campbell2 for her tireless 

support and oversight of FSIS contracts with Cornell University to develop a risk map of 

L. monocytogenes in the retail deli environment [FSIS contracts: AG-3A94-C-09-0027; 

AG-3A94-C-09-0028; and AG-3A94-C-10-0016] and with Virginia Polytechnic Institute and State 

University for both the study of L. monocytogenes transmission in retail delis [FSIS contract: 

AG-3A94-C-10-0007] and quantitative risk assessment modeling support [FSIS contract: 

AG-3A94-P-08-0166]. We also thank Kristin Holt2 for facilitating an interagency agreement between 

FSIS and CDC to collect nationwide information on worker behaviors in retail delis. 

 

7 Center for Foodborne Illness Research & Prevention

8 Center for the Science in the Public Interest

9 Food & Water Watch

10 Association of Food and Drug Officials 11 STOP Foodborne Illness 12 Food Marketing Institute

13 Oregon Department of Agriculture 14 Grocery Manufacturers Association

15 Alaska Division of Environmental Health

16 Consumer Federation of America

17 Center for Food Safety and Applied Nutrition, Food and Drug Administration

18 Virginia Polytechnic Institute and State University (Virginia Tech)

19 Joint Institute for Food Safety and Applied Nutrition

20 North Carolina State University 21 University of Arkansas 22 Purdue University

23 Cornell University
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

iv

We are grateful for insights from peer reviewers Leila M. Barraj24, Edmund A.C. Crouch25, and Renata 

Ivanek-Miojevic26. We thank David Bottimore27, Kathy Coon27, Keith Drewes27, Ruth Holt2

, Lori 

Papadakis17, and Stephanie Sarraino27 for facilitating and organizing the FDA-FSIS co-funded 

independent peer review. 

We thank Mike Mikailov28, Brian Fitzgerald28, Stuart Barkley28, and Luo Fu-Jyh28 for their invaluable 

contributions to programming and supercomputing. We also thank Eric Ebel2

, Mike Williams2, Yong 

Cao29, and Jennifer Hoeting29 for providing additional high-performance computer capacity. 

We acknowledge Wendy Fanaselle17, David Weingaertner17, David LaBarre2

, and Stacy Kish2 for support 

in the development and preparation of this report. 

We thank Susan Mary Cahill17 for careful review and editing of the draft report.

This project was supported, in part, by appointments to the Research Participation Program at the Center 

for Food Safety and Applied Nutrition, administered by the Oak Ridge Institute for Science and Education 

through an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug 

Administration. 

The considerable help and valuable expertise provided by the aforementioned notwithstanding, 

responsibility for the content of this report rests solely with the U.S. Department of Agriculture, Food 

Safety and Inspection Service; and the Food and Drug Administration, Center for Food Safety and 

Applied Nutrition. 

 

24 Exponent® Engineering and Scientific Consulting

25 Cambridge Environmental Inc.

26 Texas A&M University

27 Versar, Inc.

28 Center for Devices and Radiological Health, Food and Drug Administration

29 Colorado State University
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

v 

List of Abbreviations

AFDO Association of Food and Drug Officials 

AMIF American Meat Institute Foundation 

ARS Agricultural Research Service

cfu colony forming units

CDC Centers for Disease Control and Prevention

CFA Consumer Federation of America

CFR Code of Federal Regulations

CFSAN Center for Food Safety and Applied Nutrition 

CSPI Center for Science in the Public Interest

DHHS Department of Health and Human Services

EHS-Net Environmental Health Specialists Network

EO eating occasion

FAO Food and Agriculture Organization of the United Nations 

FCS food-contact surface(s)

FDA Food and Drug Administration 

FMI Food Marketing Institute 

FSIS Food Safety and Inspection Service 

GI growth inhibitors

GMA Grocery Manufacturers Association

GT generation time

IAFP International Association for Food Protection 

MPN most probable number 

NAFSS National Alliance for Food Safety and Security

NCBI National Center for Biotechnology Information 

NFCS non-food-contact surface(s)

NFPA National Food Processors Association

NHANES National Health and Nutrition Examination Survey 

NIFA National Institute of Food and Agriculture 

OMB Office of Management and Budget

ppm parts per million

QRA quantitative risk assessment

RAC raw agricultural commodities 

RTE ready-to-eat

sd standard deviation

U.S. United States

USDA United States Department of Agriculture

Virginia Tech Virginia Polytechnic Institute and State University

WHO World Health Organization 
 

 

 

 

 

 

 

 

 

Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report 

List of Abbreviations for Baselines and Scenarios 

The Table below introduces the abbreviations used to identify the various risk assessment model baselines 

and scenarios denoted in the figures of the Results and Discussion section of this report (Section 7). 

Abbreviations Description 

Baselines 

Multiple Niche 100W A retail deli with multiple niches on slicers, utensils, food-contact surfaces (FCS), and non-foodcontact surfaces (NFCS). Each niche contaminates its associated site, at a mean frequency of once 

per week, with a mean of 100 colony forming units (cfu) per event. 

No niche A retail deli without any niches or environmental L. monocytogenes transfer. 

Temperature Control A retail deli without any niches that maintains its deli case at ≤5°C (≤41°F) 

Incoming Growth Chub A retail deli without any niches, with one incoming ready-to-eat (RTE) product (a) whose mean 

L. monocytogenes log10 concentration of -5 log10 cfu per gram is greater than baselines (-9.2 log10 

cfu per gram) and (b) that supports the growth of L. monocytogenes. 

Incoming Non-Growth Chub A retail deli without any niches, with one incoming RTE product (a) whose mean 

L. monocytogenes log10 concentration of -5 log10 cfu per gram is greater than baselines (-9.2 log10 

cfu per gram) and (b) that does not support the growth of L. monocytogenes. 

Niche & Temperature Control A retail deli with “Multiple Niche 100W” (see above) that maintains the temperature of the deli 

case at ≤5°C (≤41°F) 

Scenarios: Worker Behaviors, Sanitation, and Cross Contamination 

Wash & Sanitize Increase the effectiveness of retail deli cleaning from simply washing to washing and sanitizing. 

Clean 8 Sporadic Doubling the number of retail deli sites sporadically cleaned from 4 to 8. 

No Sanitation Do not conduct any wiping, washing, or sanitizing. 

No Sporadic Cleaning Retail deli workers clean FCS as required by the 2009 FDA Food Code, but do not conduct any 

additional sporadic cleanings. 

No Glove Retail deli workers do not use gloves when serving customers. 

Gloves Every Serving Retail deli workers change gloves for every sale of RTE products. 

NFCS as FCS Retail deli workers clean deli NFCS as if they were FCS (i.e., every 4 hours in accordance with the 

2009 FDA Food Code). 

Transfers to 0 Scenario in which L. monocytogenes cross contamination in the retail deli would only result from 

the deli slicer (i.e., set cross contamination transfer coefficients to 0 for all sites except the slicer). 

Transfers and Slicer to 0 Scenario in which there is no L. monocytogenes cross contamination in the retail deli (i.e., set cross 

contamination transfer coefficients to 0; i.e., no cross contamination occurs for all sites, including 

the slicer). 

No-Contact Glove Case Retail deli workers do not use their hands (gloved or ungloved) to open the retail deli case (e.g., if 

a floor switch is used). 

Reduce Level Lower the mean incoming L. monocytogenes log10 concentration on all RTE products from the 

observed mean of -9.2 log10 cfu per gram to a mean of -9.5 log10 cfu per gram. 

Pre-slice Retail deli workers pre-slice all chubs of RTE product (deli meat and deli cheese) in the morning, 

after cleaning. 

vi 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

vii 

Scenarios: Worker Behaviors, Sanitation, and Cross Contamination (Continued)

Separate Slicer Retail deli workers use a separate slicer for RTE products that support growth of L. monocytogenes

versus RTE products that do not.

Separate Slicer Case Retail deli workers use a separate slicer and a separate deli case for RTE products that support 

growth of L. monocytogenes versus RTE products that do not.

Lower Env Cont Reduce transfer of L. monocytogenes among RTE products, FCS, and NFC (i.e., reduced transfer 

coefficients by 50%) in the retail deli.

Do Not Slice Onto Gloves Retail deli workers collect slices of RTE products directly on tissue paper, rather than on their

gloves.

Scenarios: Temperature Control and Growth Inhibition

Temp = 5°C Set the retail deli case temperature for all retail delis to 5°C (41°F) (i.e., in compliance with the 

2009 FDA Food Code), rather than using real-world deli-case temperatures reported by Ecosure.

No Growth (T = -5°C) Set all retail deli-case temperatures to -5°C (23°F). At this temperature, no L. monocytogenes

growth will occur.

Temp <= 5oC Use the retail deli-case temperatures observed in the Ecosure dataset at or below 5°C (41°F). This 

implies that all retail delis with deli-case temperatures exceeding the 2009 FDA Food Code

recommendation come into compliance. 

Shorten time in retail deli Retail delis reduce the length of time RTE products are held before they are sold or disposed of

from 7 to 4 days.

All GI Reformulate all RTE products sold at the retail deli that would otherwise support

L. monocytogenes growth to include growth inhibitors, to restrict the growth [same growth 

inhibitor (GI) formulation as cured ham with GI]. 

No GI Reformulate all RTE products that support L. monocytogenes growth that are sold at the retail deli 

to not include GI that would restrict L. monocytogenes growth.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

viii

Table of Contents

Acknowledgments ......................................................................................................................................... ii

List of Abbreviations .................................................................................................................................... v

List of Abbreviations for Baselines and Scenarios ...................................................................................... vi

Table of Contents ....................................................................................................................................... viii

List of Figures ............................................................................................................................................... x

List of Tables ............................................................................................................................................. xiv

Executive Summary ...................................................................................................................................... 1

1. Background ........................................................................................................................................... 5

2. Process for Conducting This Risk Assessment ................................................................................... 10

2.1. Partnership .................................................................................................................................. 10

2.2. Ensuring public participation in the process ............................................................................... 10

2.3. Collaboration with academia ...................................................................................................... 11

2.4. Scientific input and peer review .................................................................................................. 12

3. Scope and Objectives / Risk Management Questions ......................................................................... 13

3.1. Charge for the Interagency Risk Assessment, and Risk Management questions ........................ 13

3.2. Scope and objectives of the risk assessment ............................................................................... 14

4. Conceptual Model and Framework ..................................................................................................... 16

4.1. A discrete-event simulation to track L. monocytogenes in the retail environment ..................... 16

4.2. Overview of the “virtual deli,” its operation, and the impact on L. monocytogenes .................. 20

4.3. Considering model variability and uncertainty ........................................................................... 24

5. Data Collection ................................................................................................................................... 27

6. Comprehensive Description of the Risk Assessment Model .............................................................. 28

6.1. Modeling the basic processes for L. monocytogenes.................................................................. 28

6.2. Objects in the model ................................................................................................................... 43

6.3. Events in the model ..................................................................................................................... 45

6.4. From the retail deli to foodborne illness ..................................................................................... 53

6.5. Additional baseline inputs ........................................................................................................... 59

6.6. Implementation ........................................................................................................................... 64

6.7. Studying the model ..................................................................................................................... 65

7. Risk Assessment Results and Discussion ........................................................................................... 68

7.1. Risk management questions and model approaches ................................................................... 68
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

ix

7.2. Baseline analysis ......................................................................................................................... 73

7.3. Responses to risk management questions ................................................................................. 100

7.4. Verification ............................................................................................................................... 120

8. Summary of Risk Assessment Results .............................................................................................. 125

8.1. Predictions of Absolute Risk..................................................................................................... 125

8.2. Evaluation of the Impact of Differences in Baseline Conditions .............................................. 126

8.3. Scenario Analysis ...................................................................................................................... 128

9. Conclusions ....................................................................................................................................... 134

References ................................................................................................................................................. 136

Appendix 1: The Secondary Growth Model ............................................................................................. 148

Appendix 2: Consumption Data ................................................................................................................ 153
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

x 

List of Figures

Figure 1: Percentage of RTE meat and poultry products testing positive for L. monocytogenes in FSIS- inspected 

facilities, compared with incidence of listeriosis per 100,000, from CDC FoodNet surveillance ........................ 7

Figure 2: Illustration of the discrete-event cross contamination model component of the Interagency Risk 

Assessment: L. monocytogenes in Retail Delicatessens......................................................................................18

Figure 3: Illustration of stochastic decision tree within the discrete-event model of the Interagency Risk Assessment: 

L. monocytogenes in Retail Delicatessens ..........................................................................................................19

Figure 4: Diagram of “virtual deli” and cross contamination routes within the model of the Interagency Risk 

Assessment: L. monocytogenes in Retail Delicatessens......................................................................................21

Figure 5: Illustration of developed time-series based on variability within and among retail delis and uncertainty of 

existence and location of niches within the retail deli ........................................................................................26

Figure 6: The slicer model...........................................................................................................................................32

Figure 7: Illustration of the scooping model................................................................................................................34

Figure 8: The “tri linear” primary growth model and its parameters...........................................................................36

Figure 9: Simulation of customer serving times..........................................................................................................50

Figure 10: Illustration of the Monte Carlo Markov Chain used to simulate the temperature changes in retail deli 

cases....................................................................................................................................................................53

Figure 11: Distribution of serving size for deli salad (black), deli meat (blue) and deli cheese (red) for the total 

population ...........................................................................................................................................................57

Figure 12: Empirical cumulative distribution of the size of RTE food serving in a retail deli....................................60

Figure 13: General scheme of simulations...................................................................................................................64

Figure 14: Structure of the L. monocytogenes transfer matrix in the model of the Interagency Risk Assessment: 

L. monocytogenes in Retail Delicatessens ..........................................................................................................66

Figure 15. Sensitivity analysis for niches and contaminated RTE product ................................................................. 74

Figure 16: Effect of various sanitation scenarios on the mean risk per serving and relative risk in the susceptible 

population in a retail deli with multiple niches...................................................................................................78

Figure 17: Effect of various sanitation scenarios on the prevalence and relative prevalence of L. monocytogenescontaminated RTE products in a retail deli with multiple niches .......................................................................78

Figure 18: Effect of various growth scenarios on the mean risk per serving and relative risk in the susceptible 

population in a retail deli with multiple niches...................................................................................................79

Figure 19: Effect of various growth scenarios on the prevalence and relative prevalence of L. monocytogenescontaminated RTE products in a retail deli with multiple niches .......................................................................79

Figure 20: Total L. monocytogenes cfu grown, inactivated, and transferred between pairs of sites for a baseline retail 

deli with multiple niches (log10 scale). White areas indicate transfers that are not considered in the model or 

that are not meaningful. ......................................................................................................................................80
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

xi

Figure 21: L. monocytogenes cfu transferred per actual contact between each pair of sites for a baseline retail deli 

with multiple niches (log10 scale). White areas indicate transfers that are not considered in the model or that are 

not meaningful. ...................................................................................................................................................81

Figure 22: Contamination time analysis for sites in baseline retail deli with multiple contaminated niches. Upper 

graph: fraction of time each site is contaminated during a baseline simulation. Bottom graph: mean number of 

events during which contamination persists. ......................................................................................................82

Figure 23. Timeline illustration of sales, cross contamination, and subsequent risk of listeriosis ..............................84

Figure 24: Effect of various sanitation scenarios on the mean risk per serving and relative risk in the susceptible 

population in a retail deli without any niches .....................................................................................................86

Figure 25: Effect of various sanitation scenarios on the prevalence and relative prevalence of L. monocytogenes

contaminated RTE products in a retail deli without any niches..........................................................................86

Figure 26: Effect of various growth scenarios on the mean risk per serving and relative risk in the susceptible 

population in a retail deli without any niches .....................................................................................................87

Figure 27: Effect of various growth scenarios on the prevalence and relative prevalence of L. monocytogenes

contaminated RTE products in a retail deli without any niches..........................................................................87

Figure 28: Risk comparisons between niche retail deli and retail deli without any niches..........................................88

Figure 29: Effect of various sanitation scenarios on the mean risk per serving and relative risk in the susceptible 

population for retail delis with an incoming contaminated RTE product that supports growth..........................89

Figure 30: Effect of various sanitation scenarios on the prevalence and relative prevalence of L. monocytogenes

contaminated RTE products for retail delis with an incoming contaminated RTE product that supports growth

............................................................................................................................................................................89

Figure 31: Effect of various growth scenarios on the mean risk per serving and relative risk in the susceptible 

population for retail delis with an incoming contaminated RTE product that supports growth..........................90

Figure 32: Effect of various growth scenarios on the prevalence and relative prevalence of L. monocytogenes

contaminated RTE products for retail delis with an incoming contaminated RTE product that supports growth

............................................................................................................................................................................90

Figure 33: Risk comparison for niche retail deli versus retail deli with incoming RTE product that supports growth

............................................................................................................................................................................91

Figure 34: Effect of various sanitation scenarios on the mean risk per serving and relative risk in the susceptible 

population for retail delis with an incoming contaminated RTE product that does not support growth.............94

Figure 35: Effect of various sanitation scenarios on the prevalence and relative prevalence of L. monocytogenes

contaminated RTE products for retail delis with an incoming contaminated RTE product that does not support 

growth ................................................................................................................................................................. 94

Figure 36: Effect of various growth scenarios on the mean risk per serving and relative risk in the susceptible 

population for retail delis with an incoming contaminated RTE product that does not support growth.............95
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

xii

Figure 37: Effect of various growth scenarios on the prevalence and relative prevalence of L. monocytogenes

contaminated RTE products for retail delis with an incoming contaminated RTE product that does not support 

growth ................................................................................................................................................................. 95

Figure 38: Risk comparison for niche retail deli versus a retail deli with incoming product that does not support 

growth ................................................................................................................................................................. 96

Figure 39: Risk comparison for niche retail deli versus retail deli with incoming RTE product that does not support 

growth versus one that does support growth.......................................................................................................96

Figure 40. Effect of various sanitation scenarios on the mean risk per serving and relative risk in the susceptible 

population for retail deli with temperature control. ............................................................................................97

Figure 41. Effect of various sanitation scenarios on the prevalence and relative prevalence of L. monocytogenescontaminated RTE products for retail deli with temperature control.................................................................. 97

Figure 42. Effect of various growth scenarios on the mean risk per serving and relative risk in the susceptible 

population for retail deli with temperature control. ............................................................................................98

Figure 43. Effect of various growth scenarios on the prevalence and relative prevalence of L. monocytogenescontaminated RTE products for retail deli with temperature control.................................................................. 98

Figure 44. Effect of various sanitation scenarios on the mean risk per serving and relative risk in the susceptible 

population for retail deli with multiple niches and with temperature control .....................................................99

Figure 45. Effect of various sanitation scenarios on the prevalence and relative prevalence of L. monocytogenes

contaminated RTE products for retail deli with multiple niches and with temperature control..........................99

Figure 46. Effect of various growth scenarios on the mean risk per serving and relative risk in the susceptible 

population for retail deli with temperature control ...........................................................................................100

Figure 47. Effect of various growth scenarios on the prevalence and relative prevalence of L. monocytogenes

contaminated RTE products for retail deli with temperature control................................................................100

Figure 48: Relative risk comparison for sanitation options.......................................................................................101

Figure 49: Relative risk comparison for glove use ....................................................................................................103

Figure 50: Relative risk comparison for treating NFCS as FCS................................................................................104

Figure 51: Relative risk comparison for transfer coefficients....................................................................................106

Figure 52: Relative risk comparison for contact between gloves and case handle ....................................................108

Figure 53: Relative risk comparison for reducing incoming level.............................................................................109

Figure 54: Relative risk comparison for pre-slicing ..................................................................................................110

Figure 55: Relative risk comparison for separate slicers and cases...........................................................................112

Figure 56: Relative risk comparison for not slicing onto gloves...............................................................................114

Figure 57: Relative risk comparison for fixed temperature control...........................................................................115

Figure 58: Relative risk comparison for temperature control ....................................................................................116

Figure 59: Relative risk comparison for shortening the time a RTE product can be used in a retail deli department

..........................................................................................................................................................................118

Figure 60: Relative risk comparison for growth inhibitor use ...................................................................................119
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

xiii

Figure 61. Incoming and outgoing bacteria in the L. monocytogenes in retail model ...............................................121

Figure 62: Comparison of predicted model distributions with observed retail deli observations..............................122

Figure 63: Mock retail deli results [29]. Size and color intensity indicate amount of surrogate transferred from 

source to recipient location. ..............................................................................................................................123

Figure 64: Empirical cumulative density function of the serving size per eating occasion (unit: g/EO) for deli-meat 

for the total population, pregnant women, and seniors (55+): data NHANES 1999-2006. ..............................159

Figure 65: Empirical cumulative density function of the serving size per eating occasion (unit: g/EO) for deli-cheese 

for the total population, pregnant women, and seniors (55+): data NHANES 1999-2006 ...............................160

Figure 66: Empirical cumulative density function of the serving size per eating occasion (unit: g/EO) for deli-salad 

for the total population, pregnant women, and seniors (55+): data NHANES 1999-2006 ...............................160
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

xiv

List of Tables

Table 1: Illustration of site interactions and cross contamination while serving a customer.......................................22

Table 2: Growth rate (μ, h-1) and generation time (GT) of various RTE foods modeled in this risk assessment ........40

Table 3: Distribution of the predicted growth (log10 increase) during a 7-day storage at 10°C (50°F) .......................41

Table 4: Sequence of events when serving deli meat or deli cheese (derived from [28])............................................48

Table 5: Sequence of events when serving deli salad (derived from [28]).................................................................. 49

Table 6. Observed data for calculation of customer serving time................................................................................49

Table 7: Translation of the basic events in terms of basic processes...........................................................................51

Table 8: Raw storage temperature data for “Sliced Meat” (°F and °C.)......................................................................52

Table 9: Parameter of the fitted ln-normal distributions..............................................................................................54

Table 10: Fitted Laplace distribution of the refrigerator temperature data in Fahrenheit and Celsius......................... 56

Table 11: Fitted distribution of time to first consumption of RTE food, using RTI International data .......................56

Table 12: Summary statistics of the empirical distribution of serving sizes, as simulated in the L. monocytogenes

retail model (g/eating occasions) ........................................................................................................................57

Table 13: Characteristics of the distribution of bacteria in contaminated chubs (2,270 grams) according to the mean 

of the log10 normal-Poisson distribution .............................................................................................................61

Table 14: Sales and characteristics of the RTE products.............................................................................................62

Table 15: Characteristics of the RTE products............................................................................................................62

Table 16: Mean (standard deviation) of the log10 of the transfer coefficients for L. monocytogenes at retail .............63

Table 17: Comparison of expert elicitation to cross contamination model structure.................................................124

Table 18: Predicted absolute risk of invasive listeriosis per serving of ready-to-eat food sliced or prepared and sold 

at retail delis......................................................................................................................................................126

Table 19: Predicted percent change in risk of invasive listeriosis per serving of ready-to-eat food sliced or prepared 

and sold at retail delis for the susceptible population according to various scenarios, as estimated by the 

“L. monocytogenes in retail delicatessens” risk assessment model...................................................................132

Table 20: Food items considered as including “Deli Meat” ......................................................................................154

Table 21: Food items considered as including “Deli Cheese”...................................................................................156

Table 22: Food items considered as “Deli Salad”. ....................................................................................................156
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

1 

Interagency Risk Assessment: 

Listeria monocytogenes in Retail Delicatessens

Executive Summary

The “Interagency Risk Assessment: Listeria monocytogenes in Retail Delicatessens” provides a scientific 

assessment of the risk of foodborne illness associated with consumption of ready-to-eat (RTE) foods 

commonly prepared and sold in the delicatessen (deli) of a retail food store and examines how that risk 

may be impacted by changing common or recommended practices. This quantitative risk assessment 

(QRA) was conducted collaboratively by the Department of Health and Human Services’ (DHHS) Food 

and Drug Administration’s Center for Food Safety and Applied Nutrition (FDA/CFSAN) and the United 

States Department of Agriculture’s (USDA) Food Safety and Inspection Service (FSIS), in consultation 

with the DHHS Centers for Disease Control and Prevention (CDC) and input from industry, academic 

institutions, and consumer advocacy group stakeholders. The White House Food Safety Work Group 

identified this risk assessment as a priority. It provides information useful to those responsible for 

implementing policies, programs, and practices that target prevention of listeriosis.

Background 

Listeria monocytogenes (L. monocytogenes) is a food-safety concern, and control of this pathogen has 

long been an objective of the public health community. The CDC has estimated that L. monocytogenes

causes approximately 1,600 illnesses, 1,500 hospitalizations, and 260 deaths annually. When compared 

with other major foodborne diseases, listeriosis is a rare occurrence, but the fatality rate is very high (i.e., 

approximately 16%, compared with 0.5% for either Salmonella or Escherichia coli O157:H7). 

Cross contamination in the deli environment is thought to contribute to L. monocytogenes contamination 

of RTE foods, but little is known about the transfer of this pathogen in the retail setting. 

L. monocytogenes is present in the environment and can survive and grow in foods held at ambient and 

refrigeration temperatures. Therefore, adequate preventive controls must take into account contamination 

as well as survival and proliferation of the organism. L. monocytogenes can contaminate foods via cross 

contamination from one product to another or from the environment, or both. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

2 

Overview of Risk Assessment

The QRA simulates the retail deli environment and evaluates how various sanitary and food handling 

practices may influence the U.S. risk of listeriosis associated with consuming RTE foods that are sliced, 

prepared, or packaged in retail grocery delis. The model is unique in its ability to quantitatively link 

activities within a retail deli directly to predicted public health outcomes. The model simulates

L. monocytogenes concentration and prevalence in products sold to customers, predicts changes in 

concentrations during customer home storage, and estimates the risk of listeriosis from consumption of 

these products in the home. The population was divided into two subpopulations, for the purposes of this 

risk assessment: (1) the population with increased susceptibility (including neonates, older adults, and the 

immunocompromised) and (2) the remaining population (referred to as the general population). 

Risk Management Questions

The questions initially posed to the Interagency Retail L. monocytogenes Risk Assessment Workgroup

were: 

1. What is the exposure to L. monocytogenes from consuming RTE foods prepared in retail 

delis?

2. What are the key processes that increase contamination of RTE foods in retail delis?

3. How much is the relative risk per serving reduced according to specific risk management 

options? 

The above questions are very broad in nature and were further refined as a list of more specific questions

evaluated through scenario analyses within this risk assessment. Some of the specific ‘what if’ scenarios

were generated by FSIS and FDA risk managers, while others were provided by stakeholders. Examples 

include: 

• What impact does improved compliance with the cold holding and storage duration 

requirements found in the FDA Food Code have on the predicted listeriosis risk? 

• What impact does improved compliance with food-contact-surface sanitation have on the 

predicted listeriosis risk?

• What impact does using dedicated slicers for specific products have on the predicted 

listeriosis risk?

• What impact does reducing the presence and level of L. monocytogenes on incoming RTE 

foods have on the predicted listeriosis risk?
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

3 

Key Findings of the Risk Assessment

The key findings from this assessment of risk of listeriosis associated with RTE foods prepared and 

served in retail deli operations include:

• Control Growth. Employing practices that prevent bacterial growth dramatically reduced the 

predicted risk of listeriosis, as observed in other L. monocytogenes risk assessments. The use of 

growth inhibitors for suitable products prevents growth of L. monocytogenes in RTE foods, both 

at retail and during consumer home storage. In this risk assessment, use of growth inhibitors led 

to an overall dramatic reduction in the predicted risk of listeriosis (ca. 95%). Strict temperature 

control during refrigerated storage in retail delis did reduce the predicted risk (5-20% reduction, 

according to the baseline and the scenario). It should be noted that the impact of this control is 

lower than the impact from use of growth inhibitors, which mitigate growth of L. monocytogenes 

in RTE foods beyond the retail setting. 

• Control Cross Contamination. Cross contamination of L. monocytogenes in the retail 

environment dramatically increases the predicted risk of listeriosis. Cross contamination during 

the routine operation of the retail deli is not amenable to a simple solution. 

• Control Contamination at its Source. Increasing the concentration and transfers of 

L. monocytogenes from incoming products, the environment, or niches directly increases the 

predicted risk of illness. Increasing L. monocytogenes concentration in incoming product 

increased the predicted risk of listeriosis whether or not the contaminated RTE product itself 

supported growth. The increase in predicted risk was greater when the equivalent contamination 

occurred on product that supported the growth of L. monocytogenes. 

• Continue Sanitation. Sanitation practices that eliminate L. monocytogenes from deli area foodcontact surfaces result in a reduction in the predicted risk of illness. Cleaning and sanitizing foodcontact surfaces reduced the predicted L. monocytogenes levels in the deli area. Wearing gloves 

while serving customers reduced the estimated risk of listeriosis.

• Identify Key Routes of Contamination. The slicer is a primary source of L. monocytogenes

cross contamination to deli meats and cheeses. Control of L. monocytogenes cross contamination 

at this point during retail preparation of RTE foods reduced the predicted risk of listeriosis. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

4 

In summary, this QRA improves our understanding of L. monocytogenes in the retail deli and should 

encourage improvements to retail food-safety practices and mitigation strategies to further control

L. monocytogenes in RTE foods. The ‘what if’ scenarios modeled in this QRA provide insight on how 

cross contamination, sanitary practices, and temperature control impact the predicted risk of listeriosis. 

This QRA is based on an extensive amount of information gathered through partnerships with academia 

and input from stakeholders. Additional data would be useful to further explore how more specific retail 

practices and conditions (e.g., equipment design) impact the risk of listeriosis. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

5 

Interagency Risk Assessment:

Listeria monocytogenes in Retail Delicatessens

The “Interagency Risk Assessment: Listeria monocytogenes in Retail Delicatessens” provides a scientific 

assessment of the risk of foodborne illness associated with consumption of ready-to-eat (RTE) foods 

prepared in retail delicatessens (delis) and examines how that risk may be impacted by changes to current 

practices. This risk assessment was conducted collaboratively by the Department of Health and Human 

Service (DHHS), Food and Drug Administration’s Center for Food Safety and Applied Nutrition 

(FDA/CFSAN), and United States Department of Agriculture’s (USDA) Food Safety and Inspection 

Service (FSIS), in consultation with the DHHS Centers for Disease Control and Prevention (CDC), and 

input from industry, academic institutions, and consumer advocacy group stakeholders. The conduct of 

this risk assessment was identified as a priority by the White House Food Safety Work Group [1] and will

be used to evaluate current policies, programs, and practices intended to protect public health through the 

prevention of listeriosis. 

1. Background

L. monocytogenes is a food safety concern, and control of this pathogen has long been an objective of the 

public health community, including government, academia, industry, and consumer advocacy groups. The 

CDC [2] has estimated that L. monocytogenes causes approximately 1,600 illnesses, 1,500 

hospitalizations, and 260 deaths annually. When compared with other major foodborne diseases, 

listeriosis is a rare occurrence, but the fatality rate is very high (i.e., approximately 16% compared with 

0.5% for either Salmonella or Escherichia coli O157:H7). 

To prevent listeriosis in the U.S., it is important to identify the foods that pose the greatest risk of the 

illness, the most effective retail practices for controlling L. monocytogenes, and the changes in processing, 

handling, and/or preparation practices that can improve the safety of foods associated with the illness. 

Risk assessment provides a useful framework for integrating scientific research and data and evaluating 

the public health implications of changes in food safety practices and policies. 

During the past decade, FSIS and FDA have conducted several risk assessments to guide federal policies 

intended to control and prevent listeriosis in the United States. In 2003, the FDA and FSIS developed a 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

6 

QRA to determine the relative risk of listeriosis that 23 categories of RTE foods pose to the total U.S. 

population and 3 age-based subpopulations [3]. That 2003 risk assessment supported the findings of 

epidemiologic investigations of sporadic illnesses and outbreaks of listeriosis. The risk assessment 

identified and quantified the factors that affect exposure to L. monocytogenes, including (1) amount and 

frequency of consumption of the food, (2) frequency and levels of L. monocytogenes in the food, (3) 

potential of the food to support growth of L. monocytogenes, (4) refrigerated storage temperature, and (5) 

duration of refrigerated storage before consumption. The 2003 risk assessment identified several RTE 

foods as posing a high risk, per serving, including deli meats, soft cheeses, pâté, and smoked seafood. Of 

these RTE foods, deli meats were estimated to account for the most – approximately 67% – of all 

listeriosis cases per year in the U.S. [3]. 

Following the release of the FDA/FSIS risk assessment in 2003, FDA and CDC issued an Action Plan to 

reduce the risk of L. monocytogenes, which was subsequently updated in 2008 [4]. The 2008 update 

provides a list of FDA's activities within six areas: (1) develop and revise guidance for processors that 

manufacture or prepare RTE foods, for retail and food service, and for institutional establishments; (2) 

develop and deliver training and technical assistance for industry and food safety regulatory employees; 

(3) enhance consumer and health care provider information and education efforts; (4) review, redirect, 

and revise enforcement and regulatory strategies; (5) enhance disease surveillance and outbreak response; 

and (6) coordinate research activities to refine the risk assessment, enhance preventive controls, and 

support regulatory, enforcement, and educational activities. FDA's activities related to the 2008 Action 

Plan are publicly available [4]. Examples of these activities include (1) two draft guidance documents

issued for public comment in 2008: a draft “Compliance Policy Guide Sec. 555.320 Listeria 

monocytogenes” and a draft “Guidance for Industry: Control of Listeria monocytogenes in Refrigerated or 

Frozen ready-To-Eat Foods,” (2) modification of the 2005 FDA Food Code to amend the date-marking 

provisions and cold-holding times and temperatures, and (3) a public health education campaign to 

provide advice to consumers about refrigerator temperatures, to prevent foodborne illness, including 

listeriosis.

Also in response to the findings of the 2003 FDA/FSIS risk assessment, FSIS conducted a complementary 

risk assessment to evaluate which food safety interventions during the processing of RTE meat and 

poultry products are most effective in preventing listeriosis [5]. This FSIS QRA revealed that formulating 

RTE products with growth inhibitors and using post-lethality interventions, combined, were more 

effective in preventing foodborne illness, compared with using either of these interventions alone or with 

testing and sanitizing food-contact surfaces (FCSs). These findings directly formed the scientific basis of 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

7 

FSIS’s interim final rule for L. monocytogenes, which encourages federal establishments to adopt more 

effective food safety interventions during the production of RTE meat and poultry products (9 CFR 430, 

68FR 3422; June 6, 2003). FSIS also used these findings and those from the 2003 FDA/FSIS risk 

assessment to guide its verification sampling programs, whereby RTE meat and poultry processing 

establishments (9 CFR 430) with less effective L. monocytogenes controls are sampled more frequently 

[6]. These findings were used to inform FSIS’ compliance guidance to industry [7]. To aid in 

implementation of the interim final rule, FSIS provided specialized training to its inspection workforce. 

These policies and programs have resulted in industry adoption of more stringent L. monocytogenes

controls during the processing of RTE meat and poultry products in the U.S. Correspondingly, FSIS has 

observed a steady decline in the number of L. monocytogenes-positive samples from its in-plant testing 

programs, an indication that interventions during processing to mitigate risks from RTE meat and poultry 

products are succeeding (Figure 1).

Figure 1: Percentage of RTE meat and poultry products testing positive for L. monocytogenes in FSISinspected facilities, compared with incidence of listeriosis per 100,000, from CDC FoodNet surveillance

(Source: [8] and [9]).

Despite a decline of L. monocytogenes in RTE meat and poultry products tested in processing plants over 

the past several years, epidemiologic data from the CDC have shown a steady incidence of listeriosis in 

the U.S. [10, 11] (Figure 1). Recent estimates of listeriosis incidence did not meet the Healthy People 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

8 

2010 target of 0.24 cases per 100,000 population [12]. [Note: The 2020 target is 0.20 cases per 100,000 

population [13]. 

The lack of a decline in listeriosis cases in the U.S. – despite a concurrent, dramatic decline in the 

percentage of RTE meat and poultry products (primary foodborne vehicles for L. monocytogenes [3]) that 

test positive for L. monocytogenes in meat- and poultry-production facilities – suggests contamination of 

RTE products at retail or in the consumer’s home. Surveillance studies conducted by industry and 

academia [14, 15] have indicated that the prevalence of L. monocytogenes is approximately seven times 

higher in deli meats sliced at retail, compared with those sliced and packaged at federally inspected 

processing facilities. These surveys also indicated higher levels of L. monocytogenes on RTE meats sliced 

or packaged at retail. This difference in L. monocytogenes contamination was further quantified by an 

FSIS comparative risk assessment that indicated that approximately 83% of the listeriosis cases attributed 

to deli meat were associated with deli meats sliced at retail [6, 16]. An independent study by Cornell 

University also showed that the majority of listeriosis cases attributed to deli meats were associated with 

those sliced and served at retail delis [17]. 

In addition, as part of a 10-year study of the occurrence of foodborne illness risk factors in retail and 

food-service establishments, FDA collected data on food safety practices in food stores, including retail 

delis, in 1998, 2003, and 2008. The authors of the study looked for trends that would indicate whether 

practices were improving or regressing over the 10-year timeframe. The report on the 2008 data collection 

revealed that, for retail delis, the foodborne illness risk factor most in need of attention was “Improper 

Holding for Time and Temperature” [18]. In 60% of the 98 delis visited, at least one observation was 

made in which food requiring temperature control was not held at 41oF (5oC) or below, as specified in the 

FDA Food Code [18]. Similar non-compliance with temperature control of retail deli cases was suggested

in a study by Ecosure [19]. 

Moreover, the analysis of trends in retail practices during the 10-year study period revealed no 

statistically significant change in the overall percentage of compliance with the FDA Food Code for all 

risk factors combined [20]. However, a statistically significant improvement in the poor personal hygiene 

risk factor was observed, including an improvement in preventing bare-hand contact with RTE foods. 

Despite this positive trend, in roughly 50% of the 98 delis visited in 2008, at least one employee was 

observed failing to wash his or her hands at the time or to wash them in the manner recommended in the 

FDA Food Code. Also, improper temperature holding is one of several factors that may contribute to an 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

9 

increased risk of listeriosis [3]. Another is improper sanitation of slicers [21-23]. The extent to which 

these contribute to listeriosis is not well understood. 

Prior to this QRA, little was known about how L. monocytogenes contamination of RTE foods occurs in 

retail delis. L. monocytogenes strains are regularly found and often widely distributed in retail facilities 

[24, 25]. Retail practices may result in cross contamination either from one RTE product to another or 

through contamination from the retail environment, or both. Retail practices may also contribute to higher 

levels of L. monocytogenes on RTE foods [14, 15]. A recent QRA suggests that retail cross contamination 

of RTE foods has the potential to increase the risk of listeriosis considerably and that frequency of cross 

contamination has the greatest impact on the risk [26]. In addition to cross contamination, likely 

contributors to L. monocytogenes contamination and to growth of the bacteria on RTE foods at retail 

include improper holding temperatures and insufficient sanitary practices [27]. Retail food establishments 

are required to comply with a number of food safety requirements that are designed to mitigate the risk of 

foodborne illness (e.g., cold holding, date marking, specified methods and frequency of cleaning 

surfaces). However, the extent to which these requirements and other industry best practices mitigate the 

risk of listeriosis is not well understood. 

Given that there are several studies identifying retail delis as contributing to the risk of listeriosis from 

RTE foods in the U.S., and given the limited understanding of the extent to which certain retail food 

safety practices mitigate these food safety risks, the White House Food Safety Work Group identified the 

conduct of a food safety risk assessment as a priority, to guide efforts to prevent L. monocytogenes cross 

contamination at retail, for protection of the public health30. This interagency risk assessment fulfills this 

White House priority through in-depth evaluations of the extent to which certain retail food safety 

practices mitigate the risk of listeriosis and identifies practices that contribute to the risk. 

 

30 In 2009, the Federal Food Safety Workgroup identified the conduct of this interagency risk assessment as a food 

safety priority involving efforts to collaborate across federal agencies, with industry and consumer groups, and with 

the states. This risk assessment has remained a priority as highlighted in the Federal Food Safety Work Group 

Progress Report (December 2011). 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

10 

2. Process for Conducting This Risk Assessment

In the planning and conduct of this risk assessment, the Working Group pursued a unique partnership of 

government agencies, academia, industry, and consumer groups. The Working Group had four primary 

goals for the conduct of this risk assessment:

1) Shared partnership between FSIS and FDA in all aspects of the development of the project

(e.g., planning, budgeting, data acquisition, model development, peer review, and outreach); 

2) Engagement of consumer groups, retail and food industry [including Consumer Federation of 

America (CFA), Center for Science in the Public Interest (CSPI), American Meat Institute 

Foundation (AMIF), Food Marketing Institute (FMI), Grocery Manufacturers Association 

(GMA), and the Association of Food and Drug Officials (AFDO)], throughout the entire 

process; 

3) Collaboration with academia and researchers [including Cornell University, the University of 

Maryland, Virginia Polytechnic Institute and State University (Virginia Tech)] to fill 

identified, specific data needs; and

4) Scientific input and review through frequent presentations of the model and data analyses at 

scientific conferences and through a rigorous, independent peer review. 

2.1. Partnership 

FSIS and FDA formed an interagency workgroup, shared resources, and collaborated in the development 

of this risk assessment. The group met frequently and worked together to commission, collect, and 

analyze data; obtain stakeholder and public input; develop and refine the risk assessment model; co-fund 

the peer review; and develop presentations, written communications, and reports. 

2.2. Ensuring public participation in the process

In June 2009, FSIS and FDA held a meeting to garner input from the public and engage stakeholders at 

the outset. The Agencies discussed the scope and objectives of the risk assessment (74 Federal Register, 

Vol 74, No 109, June 9, 2009 27276-27278) and invited public comment and submission of scientific data 

and information (Federal Register Notice, Vol 74, No 12, January 21, 2009. 3617-3619; Federal Register 

Notice, Vol 74, No 165, August 27, 2009. 43714-3619). Comments were received from AMIF, GMA, 

and CSPI.

During the course of the risk assessment, the project was presented to various stakeholders; notably 

AMIF, FMI, GMA, CSPI, CFA, and AFDO. Stakeholder recommendations and suggestions were taken 

into consideration. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

11 

2.3. Collaboration with academia

Studies to collect data for this risk assessment were undertaken in collaboration with the University of 

Maryland, Virginia Tech, and Cornell University. Trade associations, including FMI and AMIF, 

contributed to the planning and conduct of some of these studies. Specific studies include: 

• Retail employee behavior studies. FDA, the University of Maryland, and the Joint Institute 

for Food Safety and Applied Nutrition conducted an observational study of retail deli food 

handling and sanitation practices in nine retail delis in the D.C. metro area [ ]. This 

time-series study of retail behaviors over the course of a day formed the basis for the sequential 

“events” modeled at retail in this risk assessment. The FMI was instrumental in facilitating the 

conduct of this study. Additional data from 300 retail delis in 5 states (New York, including 

New York City, Tennessee, California, Minnesota, and Rhode Island) are currently being 

gathered through a follow-on study conducted through a collaborative effort between FSIS and 

the CDC with Environmental Health Specialists Network (EHS-Net) state partners. This 

subsequent study was cleared by the Office of Management and Budget (OMB) in December 

2012, and data from this study will be used in future updates of this risk assessment. 

28

• L. monocytogenes transmission studies. A mock deli was set up at Virginia Tech, to study the 

dynamics of L. monocytogenes by semi-quantitatively evaluating transfer during events and 

actions as RTE deli products are prepared, sliced, and/or packaged in retail delis [ ]. 

Additional work was funded by the National Institute of Food and Agriculture at the University 

of Arkansas [

29

30]. 

• L. monocytogenes contamination in the retail environment

o A risk mapping of L. monocytogenes in a retail environment was developed by Cornell 

University through elicitation of expert opinion, to validate where L. monocytogenes

occurs in a retail facility [31]; 

o Cornell University also collected data on environmental L. monocytogenes

contamination in 30 retail delis in Indiana, New York, and North Carolina, during 

pre-operational and operational activities. This study has been completed, and a 

manuscript is in preparation. AMIF and FMI have extended this study, in collaboration 

with Purdue University, to further evaluate the effectiveness of interventions to prevent 

or control L. monocytogenes in retail delis. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

12 

2.4. Scientific input and peer review

In keeping with OMB’s Final Information Quality Bulletin for Peer Review (Federal Register Notice, 

Vol. 70, No. 10, January 14, 2005. 2664-2677), FDA and FSIS are committed to ensuring the quality, 

objectivity, utility, and integrity of all agency-disseminated scientific information. Peer review is an

important procedure used to ensure that the quality of published scientific information meets the standards 

of the scientific and technical community. The OMB bulletin describes peer review requirements for 

influential scientific information. A scientific assessment is defined by OMB as “an evaluation of a body 

of scientific or technical knowledge that typically synthesizes multiple factual inputs, data, models, 

assumptions, and/or applies best professional judgment to bridge uncertainties in the available 

information.” 

Consistent with the OMB peer review guidelines, the draft model of the “Interagency Risk Assessment: 

L. monocytogenes in Retail Delicatessens” was independently peer reviewed in 2010, through an external 

contract with Versar, Inc. The review focused on an evaluation of the design, logic, and mathematics of 

the risk assessment. The risk assessment model was further amended and modified in response to 

peer-reviewer comments and input from the scientific community. The reports from this external peer 

review, as well as the specific FSIS and FDA answers to the various comments, are publicly available31. 

The risk assessment model and related analyses also were presented at technical scientific meetings, 

including the 2009 and 2012 Society for Risk Analysis annual meetings (December 8, 2009, Baltimore, 

Maryland; December 11, 2012, San Francisco, California), the XVIIth International Symposium on 

Problems of Listeriosis (May 6, 2010, Porto, Portugal), the 2010 Conference on Modeling for Public 

Health Action (Centers for Disease Control and Prevention, December 10, 2010, Atlanta, Georgia), the 

2010, 2012, and 2013 International Association for Food Protection annual meetings (June 1-4, 2010, 

Anaheim, California; July 22-25, 2012, Providence, Rhode Island; July 28-31, 2013, Charlotte, North 

Carolina), and the 2012 Conference for Food Protection (April 13-18, 2012, Indianapolis, Indiana). 

 

31 FDA http://www.fda.gov/ScienceResearch/SpecialTopics/PeerReviewofScientificInformationandAssessments; 

FSIS: http://www.fsis.usda.gov/wps/portal/fsis/topics/science/risk-assessments/risk-assessments 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

13 

3. Scope and Objectives / Risk Management Questions

3.1. Charge for the Interagency Risk Assessment, and Risk Management questions 

Among the essential duties of risk managers are to determine what hazards or practices present more risk 

than society is willing to accept and to consider what control options are available [32]. These options 

must be effective and efficient in mitigating risks. The risk managers usually present the risk assessors 

with several options (posed in the form of questions) to be evaluated. The risk assessors evaluate the 

options, to determine and compare the extent to which they may reduce the public health risk. 

At the outset of this risk assessment, three questions important to risk managers were considered:

1. What is the exposure to L. monocytogenes from consuming RTE foods prepared in retail 

facilities?

2. What are the key processes that increase RTE food contamination at retail?

3. How much is the relative risk per serving reduced when specific risk management practices 

are implemented?

These broad risk management questions were further expanded as a list of more specific questions to be 

evaluated (e.g., via scenario analyses) within the risk assessment. Some of the questions were generated 

by FDA and FSIS risk managers, and others were provided by stakeholders. These include risk 

management questions related to sanitation, retail behavior, and levels of L. monocytogenes on RTE 

products entering retail delis for further preparation. 

1) What is the public health impact of more frequent or extensive retail deli cleaning procedures than 

those specified in the 2009 FDA Food Code? 

2) What is the potential public health impact of increasing the use of single-service gloves in the retail 

environment? 

3) What if scale touch pads, refrigerator and deli case handles, and other frequently touched non-foodcontact surfaces were considered food-contact surfaces and were therefore required to be cleaned and 

sanitized at a minimum frequency? 

4) What if practices were in place so that no cross contamination occurred in delis (i.e., no further 

L. monocytogenes were added to incoming RTE products)? 

5) What if display cases were not touched with gloved or bare hands (i.e., tissues or automatic door 

open/shut were used)?
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

14 

6) What would be the potential public health impact if the level of L. monocytogenes contamination on 

RTE foods coming into the retail deli were at higher level (cfu/gram)?32

7) What would be the potential public health impact of “pre-slicing” all RTE products vs. “slicing to 

order” (hypothesis: less cross contamination occurring in the morning prior to other cross 

contamination events)? 

8) What would be the potential public health impact of using separate slicers and/or separate counters for 

RTE products that permit growth of L. monocytogenes and for those RTE products that do not support 

growth of L. monocytogenes? 

9) What would be the potential public health impact of lowering the level of environmental transfers from 

environment to food? 

10) What if food workers did not slice RTE products directly onto their gloved hands? 

11) What is the potential public health impact of L. monocytogenes growth in retail delis?

12) What would be the potential public health impact of complete compliance to the cold-holding 

requirements for certain RTE foods in deli cases [i.e., 2009 FDA Food Code guidance: hold at 41°F 

(5°C) or less]? 

13) What would be the public health impact of shortening the time a RTE product can be used in a deli 

department (i.e., before it is discarded)?

14) What would be the potential public health impact if all (or no) RTE products (e.g., deli meats and deli 

salads) coming into the deli were formulated with growth inhibitors? 

3.2. Scope and objectives of the risk assessment

The objective of this risk assessment is to assess the risk of foodborne illness associated with current 

practices and to examine how that risk may be impacted by mitigations that may reduce or prevent

L. monocytogenes growth or contamination in RTE foods prepared in retail deli settings. 

The risk assessment is designed to cover RTE foods that are: i) regulated by both FSIS and FDA; ii) 

sliced, prepared, and/or packaged in the retail deli environment and consumed in the home, such as deli 

meats, cheeses, and deli-type salads; iii) sold in a retail deli environment, which refers to a range of retail 

types, such as delicatessen departments of major and large grocery chains, supermarket facilities, and 

other groceries (i.e., multipurpose, independent, small, or local facilities). Restricting the scope of the risk 

 

32 This scenario would evaluate the potential increased risk posed from an increased contamination level of 

L. monocytogenes in RTE foods at retail delis. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

15 

assessment to foods that are intended to be “consumed in the home” means that restaurants or other 

establishments where the RTE foods are consumed on-site were not included.

The risk assessment model simulates the retail environment and evaluates how changes in various retail 

sanitary and food handling practices may influence the U.S. risk of listeriosis from consuming RTE foods 

that were sliced, handled, or prepared in retail grocery delis. The model also predicts which mitigation

strategies confer the greatest benefits in reducing the risk of listeriosis. This may provide risk managers 

with information needed to inform retail food safety decisions regarding policy changes for retail facilities 

and promote industry “best practices.” 

This risk assessment is intended to be used to refine retail food safety practices and mitigation strategies, 

to further control L. monocytogenes in RTE foods.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

16 

4. Conceptual Model and Framework

The risk assessment model is unique in its ability to quantitatively link activities and changes in retail deli 

practices to public health outcomes. Model inputs are the stochastic working routines of deli workers,

L. monocytogenes concentrations of incoming product, environmental contamination of food-contact 

sites, and cross contaminations among those sites. The model simulates the L. monocytogenes

concentrations and prevalences in products sold to customers, predicts changes in concentrations during 

customer home storage, and estimates the risk of listeriosis from these sales. By serving as a “virtual 

deli,” the model allows for quantitative assessment of listeriosis risks from activities or proposed changes 

to the retail deli operation. 

To estimate risk per serving, the processes (notably, cross contamination, bacterial growth, and/or 

bacterial inactivation/removal) that lead to the level of bacterial contamination present when the RTE 

product is sold have to be considered. A specific discrete-event simulation model was developed for this 

purpose. The output of the discrete-event model is a distribution of number of L. monocytogenes per RTE 

product sold by the retail deli. An estimate of growth of L. monocytogenes on RTE product includes 

growth during transport from the retail deli to the home, as well as growth during storage in the home’s 

refrigerator. The risk of listeriosis, per serving, is eventually derived from the concentration in the RTE 

product at the time of consumption, the serving size, and a dose-response model. 

4.1. A discrete-event simulation to track L. monocytogenes in the retail environment

Cross contamination is an important process to model in food safety risk assessments. Cross 

contamination at retail has the potential to change the final dose at consumption and to lead to a greater 

number of contaminated servings of food leaving the deli. Because L. monocytogenes can grow at 

refrigerated temperatures, initial low levels of bacteria resulting from cross contamination could grow to 

higher levels during retail storage and consumer transport and storage. Cross contamination also can

increase the level of L. monocytogenes on food already contaminated with the bacterium, again increasing 

risk of illness. Given these various factors, risk assessments that incorporate cross contamination 

modeling have greater data needs and require simulation of a broader number of variables than do typical 

quantitative microbial risk assessments. 

Cross contamination is defined here as the transfer of bacteria from one food compartment or site to 

another. It is commonly used to describe the transfer of pathogens among different food groups and 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

17 

environmental surfaces during food production. Cross contamination models affect the analysis of 

exposure in the risk assessment directly. In addition to the time-temperature growth modeling common to 

most risk assessments, models of cross contamination in food usually distinguish between variables or 

compartments that need to be simulated and events or handling procedures that allow the bacteria to 

transfer from one compartment to another [e.g., 33, 34-37]. For each compartment, the bacterial 

concentrations are modeled through time. Examples of compartments for a retail environment might 

include: 

• different food groups, with possible distinction of surface concentrations versus interior 

concentrations; 

• workers’ hands and clothing;

• slicers and other equipment;

• food-contact surfaces, such as countertops; and 

• other environmental locations (e.g., refrigerated storage areas and floors).

The compartments established for a food processing plant may be different from those established when 

modeling a retail deli environment. 

Events over time that cause bacteria to be transferred from one compartment to another also must be 

simulated. Examples of events in the retail environment might include: 

• handling chubs while transferring from storage to countertop and then from countertop to slicer;

• slicing a chub;

• washing hands; 

• cleaning equipment; or

• cleaning food-contact surfaces.

The compartments that are impacted and the frequency of each event are part of the cross contamination 

model input. 

A discrete-event-type model was selected as the most appropriate framework for the charge of this risk 

assessment. In discrete-event simulation, the operation of a system is represented as a chronological 

sequence of events. Each event occurs at an instant in time and marks a change of state in the system. 

Cross contamination occurs when specific sites are brought in contact (e.g., when a chub is placed on a 

slicer). Bacterial transfer occurs only at these discrete events. A major advantage of this framework is the 

flexibility and granularity that the approach provides. Additional events can be inserted or several events 

can be merged into one without changing the overall model. The process is illustrated in Figure 2. A 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

18 

major event is selected stochastically (e.g., serving a customer or cleaning the deli area). If appropriate, 

this major event can be broken down into a series of more granular activities (e.g., removing a chub from 

the case or slicing a chub). This sequence of events can also be developed stochastically, as shown in 

Figure 3. 

Each ‘YES/NO’ branch can be based on probabilities. The level of granularity can be modified as needed. 

This flexibility is especially important, because it enables risk management questions to be added. 

Figure 2: Illustration of the discrete-event cross contamination model component of the Interagency Risk 

Assessment: L. monocytogenes in Retail Delicatessens
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

19 

Figure 3: Illustration of stochastic decision tree within the discrete-event model of the Interagency Risk 

Assessment: L. monocytogenes in Retail Delicatessens
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

20 

4.2. Overview of the “virtual deli,” its operation, and the impact on L. monocytogenes

A graphical depiction of the “virtual deli” model, along with possible L. monocytogenes transfer routes, is 

provided in Figure 4. The retail deli food worker is depicted on the lower left. 

Food RTE products are shown on the left. The current model is designed for three major food categories: 

deli meats, deli cheeses, and deli salads. Although not shown here, each of these food categories is broken 

down into more specific types. Each of these specific RTE products has associated growth rates and 

probability of being sold. Each RTE food also is tracked for age of the product. Older product is disposed 

of in this model (see section 6.5.2). 

Sites within each retail deli are shown on the right. Vertical arrows at a site indicate the possibility of

L. monocytogenes growth (up arrow) or removal by cleaning (down arrow). In practice, only

L. monocytogenes growth for RTE products was actually used for model scenarios. Asterisks at a site 

indicate the possibility of a niche. The model is flexible, in that any site may harbor a niche. Those shown 

are illustrative of the multiple niche scenarios described below. Arrows between sites, workers, and RTE 

products indicate the potential cross contamination routes. RTE food servings leaving the retail deli (i.e., 

RTE products sold) are depicted in the upper left.

The risk assessment model is flexible also in that it readily allows the addition of new RTE products, 

sites, or transfer routes. For example, this risk assessment model currently includes a floor as one of the 

sites, but no transfer of L. monocytogenes route exists from the floor to RTE food, based on a retail deli 

observational study [28]. However, such a site, event, and transfer could be readily added to this flexible 

risk assessment model. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

21 

Figure 4: Diagram of “virtual deli” and cross contamination routes within the model of the Interagency Risk 

Assessment: L. monocytogenes in Retail Delicatessens

The diagram in Figure 4 appears complicated, with a large number of cross contamination routes. 

However, the discrete-event model framework considers a limited number of site interactions at any time, 

over a long period. Table 1 depicts this scenario for a “serving a customer” event. At the beginning of the 

event, the L. monocytogenes concentrations for each site and chub are known. The first activity is to wipe 

down the slicer, which reduces the concentration of L. monocytogenes at that site (note the down arrow). 

The next activity is for the worker to wash his or her hands and put on gloves. This reduces the 

concentration on the worker’s hands and adds a new location (glove) to track. The third activity is to get 

the chub from the case. This brings in contact the worker’s gloves and the case handle, with subsequent 

potential cross contamination between worker’s gloves and the case handle. The fourth activity is to slice 

the chub. This allows cross contamination among the worker’s gloves, slicer, chub, and future serving. 

The fifth action is to weigh the serving and touch the scale. Cross contamination can occur between the 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

22 

worker’s gloves and the scale. Finally, the chub is placed back in the case, with a contact between the 

gloves and the case. At each stage, the number of sites involved and level of contamination being updated 

is small, but the cumulative effect is a mechanistic model of cross contamination, over time, in a retail 

environment. 

Table 1: Illustration of site interactions and cross contamination while serving a customer

Example: Serve Customer Event

1. Wipe Slicer 

Removes some bacteria from the slicer.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

23 

2. Wash hands and change gloves 

Removes some bacteria from hands

3. Open case, remove chub, and close case

Cross contamination between gloves and case 

4. Slice onto gloves 

Cross contamination among gloves, slicer, and 

chub 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

24 

5. Touch scale 

Cross contamination between gloves and scale 

6. Open case, replace chub, and close case

Cross contamination between the gloves and 

the case

4.3. Considering model variability and uncertainty

According to international scientific recommendations [38, 39], a quantitative food safety risk assessment 

should reflect the variability in the risk and evaluate separately the uncertainty associated with the risk 

estimates. Variability represents temporal, geographic, and/or individual heterogeneity of the food safety 

risk for a given population. Uncertainty is understood as stemming from a lack of perfect knowledge 

about the risk assessment model structure and associated parameters. Variability and uncertainty should 

be treated separately, because each has different risk management implications. 

Variability explains differences from one retail deli to another – both in terms of size of the retail deli and 

corresponding amount of equipment (e.g., number of slicers, etc.) and the operating procedures used. 

Additionally, operations in the retail deli, bacterial growth, cross contamination, consumption, etc., are 

simulated individually using stochastic processes. For each retail deli, a time-series (i.e., a series of
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

25 

L. monocytogenes concentrations at each location within the retail deli and for all servings sold 

chronologically) is developed. The time series includes a sufficiently large number of servings to evaluate 

specific operating practices (i.e., the statistics of the time series have stabilized). 

The model is written as a full second order Monte Carlo model that distinguishes variability from 

uncertainty [40] (Figure 5). It was assumed that the uncertainty surrounding the existence and the 

“behavior” of the niches overwhelmed the other sources of uncertainty. It was thus decided to illustrate 

this uncertainty through the comparison of various baselines (e.g., by comparing the various scenarios 

within a retail deli with multiple niches versus within a retail deli without niches) (see section 7). 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

26 

Figure 5: Illustration of developed time-series based on variability within and among retail delis and 

uncertainty of existence and location of niches within the retail deli
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

27 

5. Data Collection 

The Interagency Retail L. monocytogenes Risk Assessment Workgroup commissioned a number of 

studies to fill specific data needs for the conduct of this risk assessment. These studies were listed in 

Section 2.3 and include retail worker behavior data (University of Maryland and CDC/EHS-Net studies), 

environmental sources of L. monocytogenes in retail delis (Cornell University), and simulation of the 

transmission of L. monocytogenes in mock retail delis (Virginia Tech). 

In addition, the Interagency Retail L. monocytogenes Risk Assessment Workgroup conducted a 

systematic review of the literature regarding the various domains covered by the considered model. This 

systematic review included scientific literature on bacterial transfer (including during the slicing process), 

bacterial growth, bacterial inactivation through cleaning and disinfection, retail data, and consumer 

handling of food. The group synthesized the available scientific evidence to derive probability 

distributions and mathematical models. 

For that purpose, the relevant peer-reviewed scientific literature was identified using the National Center 

for Biotechnology Information (NCBI) PubMed database, cross references in related published 

manuscripts, and auxiliary data sources, such as the Google® search engine. Literature searches for 

transfer coefficients (including those specific to slicers) and for cleaning and sanitizing were performed in 

June 2009 and December 2010, respectively. Initial queries for transfer coefficients were run in the NCBI 

PubMed database using the terms ‘cross contamination,’ ‘transfer,’ and ‘bacteria,’ followed by 

identification of additional manuscripts through cross-referencing in the studies identified in the initial 

query. For studies of cleaning and sanitization, the NCBI PubMed database and Google were searched 

using 23 relevant keywords, screening all NCBI PubMed results and the first 15 pages in the Google®

database for each query. This meta-analysis of available data was published as a scientific paper in the 

International Journal of Food Microbiology [37]. The probability distributions and mathematical models 

derived within this study were used to predict L. monocytogenes cross contamination and inactivation in 

the current model. 

Other in-house data collection and meta-analyses were developed in the framework of this study. These 

meta-analyses included studies on bacterial growth, including the presence of growth inhibitors, 

consumption data, temperature data, dose-response models, etc. The results are provided within this 

report, in the corresponding sections or as appendixes. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

28 

6. Comprehensive Description of the Risk Assessment Model

6.1. Modeling the basic processes for L. monocytogenes 

Within an exposure assessment, Nauta [41, 42] suggests describing and modeling the RTE product 

pathway as a succession of “basic processes” impacting the prevalence and level of bacteria in the RTE 

product. The basic processes are the six fundamental events that may affect the prevalence and/or level of 

any microbial hazard in food processing. The basic processes used in the current model are: 

- cross contamination: a transmission of bacteria from one unit (object or food) to another one. The 

terms “cross contamination” and “transfer” will be used interchangeably; 

- bacterial growth: the multiplication of microorganisms (or growth of the population); this basic 

process is a typical characteristic of quantitative microbial risk assessment;

- bacterial inactivation: sanitation process is frequently an applied food safety and food 

preservation strategy. The chemical inactivation and physical removal of bacteria via washing 

and wiping, as well as the removal of bacteria via the disposal of contaminated objects (e.g., 

putting gloves in the trash) are included in this basic process within this risk assessment;

- partitioning: occurs when a large unit is split into several units.

In the current model, bacterial growth may occur in food all along the RTE product pathway, from entry 

into the deli through to consumption. Bacterial inactivation occurs during sanitizing, washing, and 

wiping. Partitioning will be encountered during the slicing of cheese and deli meat, as well as during the 

scooping of salad from a bulk container. Eventually, transfer of bacteria from the environment, i.e., cross 

contamination, occurs in the deli. 

This section describes the general rules, models and data used to model these basic processes. The 

transfer of bacteria is a transfer of a finite number of cells. As a consequence, the number of bacteria per 

site / food / niche is considered, and not the concentration of bacteria. In other words, bacteria are tracked 

as colony forming units (cfu) at a location, not as a representative mass or area-based concentration.

6.1.1 Cross contamination

In this report, the definition of cross contamination is enlarged to include any transfer of bacteria from 

one site, food, or niche to another.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

29 

Cross contamination between two objects 

The probabilistic derivation of the model is as follow [37]: given N1, the initial number of bacteria on a 

given object (#1), and N2, the initial number of bacteria on another object (#2). T12 is the transfer 

coefficient (0 ≤ T12 ≤ 1) from object #1 to object #2, and T21 is the transfer coefficient (0 ≤ T21 ≤ 1) from

object #2 to object #1. F1, the final number of bacteria on object #1, and F2, the final number of bacteria 

on object #2, are derived stochastically using the following algorithm:

x11 ~ binomial(N1, 1 - T12) 

x21 ~ binomial(N2, T21) 

F1 = x11 + x21

F2 = N1 + N2 – (x11 + x21) 

The underlying assumptions for this model are: 

1) the two populations N1 and N2 “act” independently;

2) within each population (N1 and N2), each bacterium “acts” independently (i.e., the probability of 

transfer for all bacteria from one object to the other is equal and constant for a given cross 

contamination). Using a binomial process assumes that the result is the sum of N1 independent 

Bernoulli assays;

3) no bacteria are lost during the transfer; and 

4) the transfer coefficients T12 and T21 are independent of the initial number of bacteria. 

Various transfer models have been developed and used in the literature [5, 33-35, 43-49]. In most, the 

independence of transfer (i.e., assumption #2) is assumed. The most discussed assumption is linked to the 

independence of the transfer coefficient and the initial number of bacteria (i.e., assumption #4). Montville 

and Schaffner [50] and Fravalo et al. [51] suggest that the transfer from contaminated objects is inversely 

related to the initial load. Rodriguez et al. [52] did not confirm this observation. Eventually, Nauta [53] 

shows that the observation of a relationship between the transfer rate and the initial level of contamination 

can be explained by an artifact linked to the limit of detection. He concludes that “so far, there is no 

evidence that bacterial transfer rates are inversely related to the initial level of contamination,” and this 

assumption is used in the model. 

Transfer coefficients were thus considered as independent of the number of bacteria, while variable from 

transfer to transfer. A complete literature review was performed [37], to develop the distribution of 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

30 

transfer coefficients for various source-recipient couples (e.g., Stainless steel - Meat). The log10

33 normal 

distribution was eventually chosen on the basis of published data and our assays’ ability to reflect the 

variability of transfer coefficients for a given source-recipient couple. Given MTij (i.e., the mean of the 

log10 of transfer coefficient from the object i to the object j) and STij (i.e., its standard deviation), a transfer 

coefficient Tij is sampled for each new transfer using:

log10(Tij) ~ Normal(MTij, STij).

If the sampled value leads to Tij > 1, then Tij is set to 1 (and thus all bacteria are transferred). 

Cross contamination between more than two objects 

The model can be extended to transfers between k objects [37]. Let Ni equal the initial number of bacteria 

on object i before transfer, and Nj equal the initial number of bacteria on object j before transfer, with i,j ∈

[1, k]. Following the same notation and rationale as above, 

( )















∏ −

≠∈ ijKj

ii Ni Tij x

,

binomial~ 1,

with K = (1, .., k), and xij, the number of bacteria transferred to object j from object i, is distributed as 

























−

∑≠∈

≠

≠

ijKj

ij

ij ij

ij ij i ii T

T

x xN

,

,

, ~ multinomia ,l

.

For the same reasons as above, Fi, the final number of bacteria on object i after transfer, equals: 

. 

∑=

=

k

j

i ji xF

1

Cross contamination during the slicing process

Slicing is a complex process in terms of bacterial transfer [54]. The objective here was to derive a model 

that could mimic the cross contamination linked to the use of a slicer in retail and that could be in 

accordance with the studies developed on the subject [21-23, 54-58]. Modeling the slicing process may 

indeed be challenging: “The following are the factors that can impact the transfer of L. monocytogenes: 

(1) the compositions of deli meat (moisture, fat content, formulation, and so on), (2) the cut surface 

characteristics (texture, homogeneity) of deli meat, (3) the rotational speed or revolutions per minute 

(rpm) of the cutting blade, (4) the diameter of the blade, (5) the sharpness (or profiles) and material of the 

 

33 Note: in this document, ln is the logarithm of base e (natural logarithm) and log10 is the logarithm of base 10.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

31 

blade, (6) the back pressure from meat loaf (weight force exerted to contact blade surface by gravity 

and/or the end weight attachment), (7) the slicing speed (for example, slices per minute), (8) the contact 

angle, area, and slice thickness, (9) the microorganism (age, strain, inoculum size, capability to adapt 

different stresses, adhesion to surfaces, and so on), and (10) the environmental condition (for example, 

temperature and so on).” [58]. 

The experimental assays provided in the literature are not sufficient to model the impact of all these 

covariates. The models that are developed in the literature are empirical and cannot be used in a stochastic 

discrete-event model. A general compartmental model that is in accordance with the literature 

observations had to be derived instead. 

The model considers two parts for the chub (both meat and cheese): 

- the head of the chub (HC), contaminated by the blade during the slicing process, 

- the remainder of the chub (named here “core of the chub” CC), which may be contaminated due 

to introduction of L. monocytogenes at the manufacturing stage or due to cross contamination 

after opening. 

The bacteria that are involved in the system have three origins: 

- the contamination of the core of the chub; 

- the contamination of the head of chub; 

- the contamination of the slicer. 

The following simulation process is used: 

1. Given CC0, the number of bacteria in/on the core of the chub of mass M0, given m the mass of a 

slice and assuming a homogeneous distribution of the L. monocytogenes on/in the chub, the 

number of bacteria newly involved in the process is I0 ~ binomial(CC0, m/M). The remaining 

number of bacteria in/on the core of the chub is then CC1 = CC0 – I0. The remaining mass of the 

chub is M1 = M0 – m. 

2. The number of bacteria from the chub, C0, eventually involved in the slicing process is the newly 

involved number of bacteria I0 and the number of bacteria on the head of the chub HC0: C0 = I0 + 

HC0. 

3. During the slicing process, 

• a part of these C0 bacteria are transferred to the slicer following Cs ~ binomial(C0, a), 

with 0 ≤ a ≤ 0.5, a parameter. The remaining stay on what becomes the slice Cy = C0 – Cs; 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

32 

• a part of the S0 bacteria stays on the slicer Ss ~ binomial(S0, 1 - 2a). On average, half of 

the bacteria transferred from the slicer are transferred to the (new) head of the chub 

according to HC1 ~ binomial(S0 – Ss, 0.5), and the remaining are transferred to the slice Sy

= S0 – S1 – HC1. 

• Y = Cy + Sy is the number of bacteria on the slice. S1 = Ss + Cs is the number of bacteria 

on the slicer at the end of the slicing process. 

The process is repeated n times to obtain n slices.

Figure 6 illustrates this model, also described in Hoelzer et al. [37]. This process is in accordance with the 

experimental data that i) generally show a log linear decrease of the number of bacteria, which

contaminates successive slices of RTE products; and ii) suggest a cross contamination between the slicer, 

the chub, and the RTE product that is sold [21-23, 54-58]. 

CC1

CC0 

CC1

St

Sr

C0 : Number of bacteria from chub available for transfer at time 0

C0 = I0 + HC0

HC0: Number of bacteria on head of chub at time 0

HC0

CC1: Number of bacteria in chub core at time 1 

CC1 = CC0 – I0

Cr: Number of bacteria remaining on slice during slicing

Cr = C0 – St with St : number of bacteria transferred from slice to blade

Cr 

HC1: Contamination on head of chub at time 1 (origin: slicer blade) 

HC1 ~ binomial(S0 – Sr,0.5)

S1 

C1 : Number of bacteria on slice at time 1

C1 = Cr + (S0 – Sr – HC1)

Time step 0before slicing

Transition from step 0 to 1during slicing

Time step 1after first slice

Sr : Number of bacteria (fraction of S0) remaining on slicer blade during slicing

Sr ~ binomial(S0, 1 - 2a)

slicer

chub

chub slice

chub slice slicer slicer HC1

C0 

CC0 : Number of bacteria in chub core at time 0

I0

I0 : Bacteria in chub core available for transfer

I0 ~ binomial (CC0, m/M0) with m and M0 the mass of slice and core at time 0

S1: Number of bacteria on slicer blade at the end of the first slicing

S1= Sr + St

C1 

S0 

S0 : Number of bacteria initially present on the slicer blade

St : Number of bacteria transferred from slice to slicer

St ~ binomial(C0,a) where a equals the transfer coefficient 

Figure 6: The slicer model

(Source: [37]) 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

33 

A literature review of the available scientific articles dealing with slicers was performed. The exhaustive 

review is detailed in Hoelzer et al. [37]. The only experimental designs considered were those in which i) 

a blade is artificially contaminated, and ii) RTE products are contaminated by the blade during the slicing 

process [23]. The inferred distribution for the parameter a is a logistic distribution with location parameter 

0.07 and scale parameter 0.03. The mean and median of the distribution for a are 8.1% and 7.7%, 

respectively.

Cross contamination during scooping process 

Similarly, a model was derived for the specific process of scooping deli salad from the bulk container. 

The following simulation process is used: 

1. Given CC0, the number of bacteria in/on the salad bulk of mass M0, and given m the mass of the 

serving and assuming a homogeneous distribution of the L. monocytogenes on/in the salad, the 

number of bacteria newly involved in the process is C0 ~ binomial(CC0, m/M). The temporary 

remaining number of bacteria in/on the core of the salad bulk is then (CC0 – C0). The remaining 

mass of the salad bulk is M1 = M0 – m. 

2. During the scooping process: 

• The utensil contaminates the serving and the remaining bulk salad: a part of the U0

bacteria present on the utensil will stay on the utensil according to Uu ~ binomial(U0, 1 – 

TCus), where TCus is the transfer coefficient from the utensil to the salad. It is assumed

that, on average, half of (U0 – Uu) bacteria transferred from the utensil are transferred to 

the top of the salad according to TS1 ~ binomial(U0 – Uu, 0.5), and the remaining are 

transferred to the serving Su = U0 – Uu – TS1. 

• The serving contaminates the utensil: a part of the C0 bacteria present in the serving are 

transferred to the utensil following Cs ~ binomial(C0, TCsu), where TCsu is the transfer 

coefficient from the salad to the utensil;

• The remaining bulk salad contaminates the utensil: it is assumed that the utensil is in 

contact with a m/M1 part of the remaining salad (i.e., to R0 ~ binomial(CC0 – C0, m/M1) 

bacteria). A part of these bacteria, R1 ~ binomial(R0, TCsu), will be transferred to the 

utensil; 

3. The remaining number of bacteria in the bulk container of salad is the initial number of bacteria 

in the bulk minus the number of bacteria that were in the serving minus the number of bacteria 

that contaminate the utensil plus the number of bacteria transferred from the utensil, (i.e., CC1 = 

CC0 – C0 – R1 + TS1); 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

34 

4. The number of bacteria in the serving is the original number of bacteria from the bulk salad 

minus those that transferred to the utensil plus those that transferred from the utensil, (i.e., S1 = 

C0 - Cs + Su). 

5. The number of bacteria on the utensil at the end of the scooping process is the number of bacteria 

that were not transferred to the salad or the serving plus the number of bacteria transferred from 

the serving plus the number of bacteria that transferred from the remaining salad, (i.e., U1 = Uu + 

Cs + R1). 

CC1

CC0 

CC0 – C0 C0 Utensil

Bulk of salad

Bulk of salad serving

Bulk of salad Serving Utensil Utensil

C1

U0 

U1 

U0 The utensil contaminates the remaining bulk of salad

The utensil contaminates the serving

The serving contaminates the utensil

The remaining bulk of salad contaminates the utensil

C1 : Number of bacteria in the serving at time 1

Time step 0before scooping

Transition from step 0 to 1during scooping

Time step 1after scooping

CC0 : Number of bacteria in bulk salad at time 0

CC0-C0 : Bacteria in the remaining bulk salad available for transfer

C0 : Bacteria in serving available for transfer

U0 : Number of bacteria initially present on the Utensil

U0 : Number of bacteria initially present on the Utensil

CC1 : Number of bacteria in bulk salad at time 1

U1 : Number of bacteria initially present on the Utensil at time 1

Figure 7: Illustration of the scooping model

Cross contamination from a niche / Contamination from the environment 

A niche or harborage site is a location associated with a site where bacteria can reside and resist normal 

cleaning and sanitation procedures. Existing literature provided little insight into the development of a 

conceptual model for the transfer of L. monocytogenes from niches to RTE foods. As such, a very 

simplified model was developed to consider the presence of niches in the retail environment:

- Each niche is associated with an existing site within the model. Transfer from the niche only 

occurs to its associated site. Once bacteria transfer out of the niche to the associated site, they 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

35 

become part of the site’s bacteria count and can move to other sites through classical cross 

contamination; 

- The probability for a site to have a niche is specified by the user;

- From time to time, the niche releases a fixed number of bacteria to the site. This number is 

specified by the user; and 

- The occurrence of release is assumed to follow a Poisson process. The time to the next release 

from the niche is then assumed to follow an exponential distribution with mean λ also specified 

by the user. 

As an example, the user can specify a probability of having a niche associated with a deli case as 0.5, a 

number of transferred bacteria as 1,000 cfu, with a release of bacteria occurring, on average, every 168 

hours of operation. In this context, if a niche is present, 1,000 cfu will be transferred to the corresponding 

case, on average, every 168 hours of operation. 

Note that this concept could either simulate the presence of a niche or, similarly, the presence of a regular 

contamination from an external source. This could mimic, as an example, a food worker who would 

regularly (on average, every 168 hours of operation) place a contaminated object, such as a milk crate, on 

the food-contact surface, with a transfer of 1,000 cfu. 

6.1.2 Bacterial growth

Bacterial growth is one of the important basic processes that leads to exposure to, and risk from,

L. monocytogenes [3, 59]. 

Growth models in food

Predictive microbiology is a science whose object is to predict the size of a bacterial population according 

to the environment of the bacteria. Predictive microbiology in food has expanded in recent years and now 

provides interesting tools for risk assessment purposes [60, 61]. 

In predictive microbiology, a “primary model” is a model that predicts the evolution of the size of the 

bacterial population according to time in a given environment. The “secondary model” is a model that 

evaluates the evolution of the parameters of the primary model according to the environment.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

36 

Primary growth model 

The primary model predicts the number of cells with time. A popular growth model is the exponential 

“tri-linear” model [61, 62]: 

( ) ( )

( ) ( ) ( ) ( ) 





= −×+ ≥

= <

λ λ

λ

yty EGR tyt

yty t

max min 0 ,

0

where y(t) (log10 cfu/g) is the bacterial concentration at time t (day), λ (days) is the lag time, EGR is the 

exponential growth rate (log10 cfu day-1), and ymax (log10 cfu/g) is the maximum achievable concentration 

in the media (Figure 8). ln( ) 10

24× μ EGR = , where μ is the specific growth rate (h-1). This model has been 

described as a simple but sufficiently complex model to be used in predictive microbiology and in risk

assessment [61, 62]. 

Time (days)

y (log10 (cfu g-1))

λ

ymax

EGR

y(0)

Figure 8: The “tri linear” primary growth model and its parameters

A lag time in the growth is observed in case of rapid change in the bacterial environment. 

This model is purely deterministic (i.e., is suitable for a large number of bacteria). The stochastic (i.e., for 

a small number of bacteria) analog of the exponential phase of this model is the Yule pure birth growth 

model [63], as described by Vose [64], with the following premise: i) that individual bacteria have 

offspring on their own (e.g., by division), ii) that they procreate independently, iii) that procreating is a 

Poisson process in time, and iv) that all individuals in the population are the same. The expected number 

of offspring from an individual per unit time (over some infinitesimal time increment) is defined as μ. 

This leads to the result that an individual will have, after time t, a number of offspring that follow a 

geometric(exp(-μt)) distribution. Starting with x(0) individuals (cfu), 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

37 

( ) ( ) ( ) ( ) xtx 0~ + (xNegBin ,0 exp − μt )

where NegBin(n, p) is the negative binomial distribution34 with size parameter n and probability 

parameter p. Note that, as desired, the expected value of x(t) is35

x(0) exp(μt). Then, y(t) = y(0) + EGR × t

and the expectation of the stochastic model is the deterministic model.

Secondary growth model on EGR or μ

The Gamma concept [65] 

The secondary models predict the change in the primary model parameters according to a change in the 

growth environment. Many secondary models are available [60, 61]. Most of these models may be 

grouped either as polynomial models or models from the gamma concept family. 

The gamma concept considers the impact of multiple environmental factors on bacterial growth. The 

principles of the gamma concept [65] are: 

- A μopt (or an 

)10ln(

24 opt EGRopt

× μ = ) parameter is specified. This is the growth rate obtained when all 

environmental parameters are optimal for the bacterial growth; 

- For each considered environmental parameter xi (e.g., temperature (T), pH, water activity (aw),

nitrite concentration (nit), lactic acid concentration (LAC), and diacetate concentration (DAC)), a 

function γi(xi) is defined, with 0 ≤ γi(xi) ≤ 1 reflecting the impact of this environmental parameter 

on the growth. An additional function ξint is defined, to consider the interaction among 

parameters.

- Then, ( ) ( ) n

i

iiopt ,..., xxx 1int ξγμμ















 = ∏ in the considered environment; i.e.,

( ) ( ) ( ) ( ) ( ) ( ) ( T apH nit LAC DAC DAC w LACnitapHT DAC) Topt pH w nitwa LAC ,,,,, ξ int ⋅⋅⋅⋅⋅= γγγγγμμ ⋅ γ ⋅

The advantage of the gamma concept is that it allows independent consideration of a large number of 

environmental parameters. It is extensively used in the predictive microbiology domain and claimed to be 

universal, allowing Listeria growth to be modeled in a variety of different RTE products, if some of their 

 

34 Because the sum of n independent geometric distributions with parameter p is a negative binomial distribution 

with parameter n and p. 35 The expected value of a NegBin(n, p) distribution is ( ) 1− ppn . The expected value of x(t) is then 

x( ) ( ) (10 exp −− μ ) ( ) exp μ ( ) ( ) ( )( ) ( ) ( ) ( ) =+− 00 exp μ =+− 001 exp μtxxtxxtt 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

38 

characteristics are known [66]. This gamma concept has already been used in QRAs (e.g., in an 

Australian risk assessment for L. monocytogenes in RTE meats [67]). 

The alternatives would be: 

- the use of one exponential growth rate per food category, as was done within the 2003 FDA/FSIS 

risk assessment [3]. Nevertheless, it might be difficult to obtain some data for all kinds of RTE 

food (e.g., with and without growth inhibitors); 

- the use of only the minimum of the γi factors. This is more common in environmental modeling, 

because of concerns that the multiplicative model is overly restrictive as more factors are 

considered and that μopt thus becomes a function of the number of factors; 

- the use of polynomial models that predict the bacterial growth. The major drawback of these later 

models is that they are only applicable to the situation for which they were developed [61]. The 

polynomial models are of great interest for one or a limited number of food RTE products, but 

could not be incorporated into this model. 

The disadvantage of this gamma concept approach is that the chemical characteristics of the RTE 

products are needed to evaluate the potential for L. monocytogenes growth in a RTE product, (i.e.at least 

the pH and aw, if no preservative is present). 

The Mejlholm and Dalgaard model

The gamma concept was extended, over time, to include more and more parameters, as well as their 

interactions [68-74]. In 2009, Mejlholm and Dalgaard gathered and evaluated those modules to build an 

extensive model for growth [75]. This model included the impact of the temperature; water activity 

(calculated from the concentration of NaCl in the water phase of the RTE product); pH, concentration in 

smoke components (phenol); concentration in nitrite; concentration of dissolved CO2 at equilibrium; and

concentrations of undissociated lactic acid, diacetate, acetic acid, benzoic acid, citric acid, and sorbic acid. 

The Mejlholm and Dalgaard model [75] was used in this study, but limited to temperature, pH, water 

activity, nitrites, sodium lactate, potassium lactate, and sodium diacetate concentration and their 

interactions. The model, its parameterization, and its parameters are fully described in Appendix 1. 

Validation of the model

In the original paper, Mejlholm and Dalgaard [75] obtained bias and accuracy factors [76] of 1.4 and 1.6, 

respectively, after evaluating growth rates of L. monocytogenes in different types of meat products with 

added organic acids obtained by various authors [77-80]. A second publication from these authors 

compared the predictive values of this model against concurrent ones [66, 81, 82] for various RTE foods 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

39 

[83]. For that purpose, 1,014 growth responses of the pathogen in meat, seafood, poultry, and dairy 

products were used for validation. For the Mejlholm and Dalgaard [75] model, bias and accuracy factors 

for growth rate predictions were 1.0 and 1.5, respectively. The performance of three other models, 

including the effect of five to seven environmental parameters, was lower, with bias factors of 1.2 to 1.3. 

Less complex models that did not include the effect of acetic acid/diacetate and lactic acid were unable to 

predict growth responses of L. monocytogenes accurately in a wide range of food. 

Results

Table 2 illustrates the use and results of the growth model in various RTE foods simulated in this risk 

assessment (i.e., deli meat, cheese, and deli salad). The growth rates (h-1) and the generation time (time 

for the population to double) is affected by the intrinsic properties of the products, as well as the inclusion 

of growth inhibitors. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

40 

Table 2: Growth rate (μ, h-1) and generation time (GT) of various RTE foods modeled in this risk assessment 

T = 4°C

(39.2°F)

T = 10°C

(50°F)

RTE product 

Type (example*)

pH aw Nitrites

(ppm)

Sodium 

Lactate

(%)

Potassium 

Lactate

(%)

Sodium 

Diacetate

(%)

μ

(/h) 

GT

(h) 

μ

(/h) 

GT

(h) 

Deli Meat (Uncured Ham) 6.4 0.97 0 0 0 0 0.015 47 0.052 13

Deli Meat 

Low Growth

(Cured Ham)

6.4 0.97 150 0 0 0 0.003 210 0.017 41

Deli Meat 

No Growth

(Cured Ham w 

growth inhibitor 

[GI]) 6.4 0.97 150 0 1.65 0.12 0.000 Inf** 0.009 78

Deli Meat (Uncured Turkey) 6.3 0.96 0 0 0 0 0.012 60 0.041 17

Deli Meat 

Low Growth

(Cured Turkey)

6.3 0.96 150 0 0 0 0.002 376 0.013 52

Deli Meat 

No Growth

(Cured Turkey w 

GI) 6.3 0.96 150 0 1.65 0.12 0.000 Inf 0.004 183

Deli Meat (Uncured Bologna) 6.3 0.93 0 0 0 0 0.000 Inf 0.006 121

Deli Meat 

Low Growth

(Cured Bologna)

6.3 0.93 150 0 0 0 0.000 Inf 0.000 Inf

Deli Meat 

No Growth

(Cured Bologna w 

GI) 6.3 0.93 150 0 1.65 0.12 0.000 Inf 0.000 Inf

Deli Meat 

No Growth

(Pepperoni)

4.7 0.83 0 0 0 0 0.000 Inf 0.000 Inf

Deli Meat 

No Growth

(Salami)

5.0 0.91 0 0 0 0 0.000 Inf 0.000 Inf

Deli Cheese 

Low Growth 

(Colby)

5.2 0.95 0 0 0 0 0.002 460 0.013 54

Deli Cheese 

No Growth

(Monterey Jack)

5.3 0.93 0 0 0 0 0.000 Inf 0.001 522

Deli Cheese 

No Growth

(American)

5.6 0.92 0 0 0 0 0.000 Inf 0.000 Inf

Deli Cheese 

No Growth

(Provolone)

5.2 0.91 0 0 0 0 0.000 Inf 0.000 Inf

Deli Cheese 

Low Growth 

(Swiss)

5.2 0.92 0 0 0 0 0.000 Inf 0.000 Inf

Deli Salad (Potato) 4.6 0.998 0 0 0 0 0.000 Inf 0.000 Inf

Deli Salad 

Low Growth

(Potato w GI)

4.6 0.998 0 0 1.65 0.12 0.000 Inf 0.000 Inf

Deli Salad (Protein) 5.0 0.998 0 0 0 0 0.000 Inf 0.003 252

Deli Salad 

Low Growth

(Protein w GI)

5.0 0.998 0 0 1.65 0.12 0.000 Inf 0.000 Inf

*Note that the example is provided only as illustration purpose; **: infinite. The generation time is infinite since the growth rate is 0. 

Stochasticity in the μ parameter

The secondary model mentioned above is deterministic, in the sense that one set of environmental 

parameter leads to one expected value for μ. Augustin et al. [84] quantified the variability of growth 

parameters of L. monocytogenes obtained by challenge testing in five RTE products (vacuum-packed pork 

pie, vacuum-packed smoked herring, sliced cooked ham packed under modified atmosphere, cooked 

chicken, and surimi salad). The total variance obtained when adding different sources of variability 

(residual, between-batch, and between-manufacturer) led to a total coefficient of variation36 for μ of 45%. 

 

36 The coefficient of variation is the ratio of the standard deviation to the mean. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

41 

In order to consider these sources of variability, the μref,i parameter (the specific growth rate at a reference 

temperature) for a given RTE product i (chub, or deli salad bulk) was sampled from a normal distribution 

with mean μref and standard deviation (0.45 × μref). Negative values were set to 0. Table 3 illustrates the 

distribution of the predicted growth during 7 days of storage at 10°C. 

Table 3: Distribution of the predicted growth (log10 increase) during a 7-day storage at 10°C (50°F)

Example* 1st Quantile Median Mean 3rd Quantile

Deli Meat (Uncured Ham) 2.66 3.82 3.82 4.97

Deli Meat Low Growth (Cured Ham) 0.87 1.25 1.25 1.63

Deli Meat No Growth (Cured Ham with GI) 0.69 0.98 0.98 1.28

Deli Meat (Uncured Turkey) 2.08 2.98 2.98 3.87

Deli Meat Low Growth (Cured Turkey) 0.68 0.97 0.97 1.27

Deli Meat No Growth (Cured Turkey with GI) 0.32 0.45 0.46 0.59

Deli Meat (Uncured Bologna) 0.29 0.42 0.42 0.55

Deli Meat Low Growth (Cured Bologna) 0.00 0.00 0.00 0.00

Deli Meat No Growth (Cured Bologna with GI) 0.00 0.00 0.00 0.00

Deli Meat No Growth (Pepperoni) 0.00 0.00 0.00 0.00

Deli Meat No Growth (Salami) 0.00 0.00 0.00 0.00

Deli Cheese Low Growth (Colby) 0.65 0.93 0.94 1.22

Deli Cheese No Growth (Monterey Jack) 0.07 0.10 0.10 0.13

Deli Cheese No Growth (American) 0.00 0.00 0.00 0.00

Deli Cheese No Growth (Provolone) 0.00 0.00 0.00 0.00

Deli Cheese Low Growth (Swiss) 0.00 0.00 0.00 0.00

Deli Salad (Potato Salad) 0.00 0.00 0.00 0.00

Deli Salad Low Growth (Potato Salad with GI) 0.00 0.00 0.00 0.00

Deli Salad (RTE Meat Deli Salad) 0.00 0.00 0.00 0.00

Deli Salad Low Growth (RTE Meat Deli Salad with GI) 0.00 0.00 0.00 0.00

*Note that the example is provided only for illustration purposes. 

Other parameters

Lag time 

A lag time is observed in bacterial growth when an abrupt change in the bacterial environment is 

observed. No abrupt change in the bacterial environment in retail, during transport, and at home is 

considered in the model. The considered process consists only of slicing or scooping at retail and storage 

at home. The shifts in temperature are probably not abrupt enough to induce a lag [3]. No consensual 

model in the literature predicts lag time following transfer from a surface to food. As a conservative 

choice favoring the model that leads to a higher risk [59, 85], this risk assessment model does not take 

into account a potential lag phase in bacterial growth that may occur upon transfer of bacteria from one 

surface to another. A lag after a transfer from FCS or NFCS to food could be considered in future 

versions of this risk assessment.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

42 

ymax (Maximum Population Density)

The maximal population density is a very important parameter for prediction of the risk associated with

L. monocytogenes [86, 87]. Nevertheless, few studies have evaluated this parameter. Within the 2003 

FDA/FSIS risk assessment, for example [3], the maximal population density was related to the 

temperature. For deli meat, deli-type salads, and cheeses, the growth was limited to 5 log10 cfu/g, if the 

temperature was <5°C (41°F); to 6.5 log10 cfu/g, if the temperature was 5-7°C (41-44.6°F); and 8 log10

cfu/g, if temperature was >7°C (44.6°F). As a safe choice, it was considered that growth can reach 8 log10

cfu/g in all RTE products, including those with growth inhibitor, whatever the storage temperature.

Growth models on sites 

The growth model used on sites is the stochastic derivation of an exponential model without lag, as the 

one used for food: 

( ) ( ) ( ) ( ) 































+ − t egr xtx xNegBin ln 100~ ,0 exp

with egr ≥ 0, the exponential growth rate on the considered site. egr could be a function of the 

temperature. Currently, egr is set to 0, meaning that no growth is considered on sites. Note that a decrease 

in the number of bacteria with time may be observed on the various sites [88]. 

6.1.3 Inactivation 

In the current risk assessment model, inactivation is limited to the removal and reduction of

L. monocytogenes on surfaces as the result of cleaning operations, (i.e., wiping, washing, and 

sanitization). The inactivation process is modeled as follows. Given N, the initial number of bacteria on 

the site being treated, and W, the efficacy of the inactivation process (0 ≤ Wi ≤ 1), F, the final number of 

bacteria on the site is derived stochastically using:

F ~ binomial(N, W) 

This assumes that the bacteria are inactivated independently. W is currently sampled at each inactivation 

process from:

log10(W) ~ Pert(min, mode, max) 

where Pert is the Pert distribution [64], min, mode, and max are specific to i) the object and ii) the level of 

inactivation. Currently, three levels of inactivation are implemented: “Wipe,” “Wash,” “Wash and 

Sanitize.” Note that W is minus the expected log10 reduction of the process. If log10(W) is -1, then W is 0.1

and the expected log10 reduction is 1; this leads to an expected 10-fold decrease of the number of bacteria 

on the object. Following a complete literature review [37], (min, mode, max) is set to (-1, -0.5, 0) for all 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

43 

“wiping” processes, to (-1.5, -0.5, 0) for all “washing” processes, and to (-8, -6, -1.5) for all “washing and 

sanitizing” processes. 

6.1.4 Partitioning

The only partitioning processes in the model are: 

- partitioning a chub to a slice; and

- partitioning a serving of salad from a bulk container. 

The partitioning process has already been described in the subsection dealing with cross contamination 

during the slicing process and scooping process. Indeed, a homogeneous contamination of the chub and 

the salad is assumed. As a consequence, given N0, the number of bacteria in the chub (or the salad bulk), 

M is the mass of the chub (or the salad bulk) and m the mass of the slice (or the salad serving), while the 

number of bacteria in the slice (or the salad serving) is

N1 ~ binomial(N0, m/M) 

The number of bacteria in the remaining chub (or the salad bulk) will then be N1 – N0. 

6.2. Objects in the model

6.2.1 Food 

Three categories of food were considered in the model: deli meat, deli cheese, and deli salad. Deli meat 

and deli cheese were served following a slicing process. Deli salads were served by scooping from a bulk

container. 

Any type of RTE products within these categories may be simulated in the risk assessment model. For

example, deli meat could be ham with high potential of L. monocytogenes, uncured ham, ham with 

growth inhibitor, salami, etc. The model is flexible enough to support the addition of new RTE products. 

The following characteristics must be known or estimated for each RTE product: 

- “category”: “chub” or “salad.” When served, a “chub” will imply a process of slicing, while a 

“salad” will imply a process of scooping. “Chubs” are meats and cheeses; 

- sale characteristics summarized by 

o the probability of being present in the retail deli (for rare RTE products) or the number of 

chub/salad bulk in the retail deli (for RTE products that are present in all deli 

departments), and 

o the relative frequency of sales of the RTE product and the mass of a slice (for “chubs”);
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

44 

- some physical characteristics (i.e., the mean and the standard deviation of the mass of the chub / 

bulk); 

- some chemical characteristics [i.e., pH, water activity, nitrites (ppm), as well as the proportion 

(w/w) of sodium lactate, potassium lactate and sodium diacetate]. Those factors are used to 

estimate the growth rate in the RTE product (see section 6.1);

- mean and standard deviation of the initial log10 concentration of L. monocytogenes; 

- number of days the RTE product can be held in the retail deli after opening or preparation; 

- mean and standard deviation of the log10 of the transfer coefficients (see p. 29) of bacteria from 

these food categories to other food or sites;

- probability of having this food item pre-sliced in the morning, with a mean and standard deviation 

of the weight of RTE product that would be pre-sliced. 

6.2.2 Sites

Sites are potentially contaminated objects that are present in a deli department. Those sites were chosen 

following the observational study specifically developed for this model [28]. Currently, the following 

objects are considered: “floor,” “sink,” “handle,” “case,” generic “non-food-contact surface” (NFCS), 

“utensil” (and its “handle”), “slicer,” generic “food-contact surface,” (FCS), and “scale.” Two additional 

“sites” are associated with food workers: “hands” and “gloves.” 

The sites are characterized by:

- their initial contamination at the beginning of the simulation; 

- the probability of having a niche compartment/of being contaminated from the environment and, 

in this case, the number of bacteria transferred to the considered site during a release of bacteria 

from the niche/environment and the average time of operation between two releases (see p. 34); 

- the mean and the standard deviation of the log10 reduction of L. monocytogenes when the sites

were wiped, washed, sanitized, or washed and sanitized (see p. 42);

- the mean and standard deviation of the log10 of the transfer coefficients (see p. 29) of bacteria 

from these sites to other sites or food; 

- the exponential growth rate (egr) of L. monocytogenes on this object, currently egr = 0. 

More than one site in each category may be present in the retail deli. Additional objects could easily be 

implemented, as soon as they are associated with specific events (potential cross contamination from/to 

this object).
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

45 

Structure of the deli departments

In the model, the user can build a variety of retail-deli-department layouts or, using an associated relative 

probability, may run simulations using a set of specific deli-department layouts. A layout is characterized 

by:

- its hours of operation; 

- its number (≥1) of various sites present in the deli-department (floor, sink, handle, scale, utensil, 

slicer, FCS, NFCS); 

- the probability (0≤ p ≤1) that gloves are worn by an employee handling unpackaged foods when 

serving customers; 

- the probability (0≤ p ≤1) the niche / environmental contamination occurs in the department, 

- the time in hours between cleaning the food-contact surfaces; 

- the number of objects that are cleaned during a sporadic cleaning event.

Moreover, a matrix of contact has to be specified for a given retail deli. This matrix of contact allows one 

to specifiy which objects or food worker can be in contact with which category of food. For example, in a 

complex retail deli including three slicers, it is possible to specify that slicer #1 is used only for cheese, 

slicer #2 for meat with no growth, slicer #3 for all kinds of RTE products. This matrix of contact allows 

one to study various deli-department patterns. 

6.3. Events in the model

It is important to note that the model tracks only the actions made by, and the transfers resulting from, the 

actions of a single food worker. More complex models could be derived for multiple food workers. 

6.3.1 Main events

The main events simulated within the model are “Opening the deli,” “Closing the deli,” and “Operating 

the deli.” Each is described below. 

Opening the deli

The virtual deli is open 7 days a week, during a given number of hours. This number of hours is specified 

at the retail deli level. 

When the retail deli opens, the food items that were opened or prepared more than a specified number of 

days prior (e.g., 7 days for refrigerated RTE foods) [27]) are discarded. This mimics a date-marking 

system [27]. Additionally, when the retail deli opens, some food items may be “pre-sliced” in large 

quantity. Afterwards, pre-sliced items will be sold throughout the day. A food item is pre-sliced or not 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

46 

according to a probability of pre-slicing defined by the user (if 1: some amount of RTE product will be 

pre-sliced each morning; if 0: this food item will never be pre-sliced; if > 0 and <1: a random value will 

be drawn each morning to decide whether or not the RTE product will be sliced). The amount of RTE 

product (>0 g) that will be pre-sliced will be randomly sampled from a normal distribution with mean and 

standard deviation defined by the user. Then the RTE product will be pre-sliced using the same process as 

if a serving of this RTE product of that size were requested by a customer.

Closing the deli 

When the retail deli closes (or every 24 hours of operation, if the deli is open 24/7), all hard surfaces and 

equipment in the deli are washed and sanitized. The remaining pre-sliced RTE products are discarded.

Bacterial growth on all sites and food is considered during the time the deli is closed. 

Operating the deli

Within the operating hours, three main events are considered: i.e., “Non Deli Time,” “Sporadic Clean,” 

and “Serve Customer.” Additionally, i) the FCS are washed and sanitized regularly, according to a 

planned schedule; ii) some transfer of bacteria from the niches may occur, according to a random 

schedule.

During the “Non Deli Time” event, nothing happens concerning our considered process (except bacterial 

growth; see below). 

During the “Sporadic Clean” event, some sites are cleaned within the deli. The number n of objects 

cleaned during a sporadic clean event is characteristic of the retail deli (defined by the user). The 

algorithm selects the n sites that have not been cleaned since the longest time, with a random selection in 

case of ties. Those objects are then “washed” (i.e., not “washed and sanitized”). 

Before all major events, the algorithm checks whether or not one transfer occurred from one niche to its 

corresponding site (see p.34). 

At the end of a main event, the algorithm checks the “FCS clock.” This clock is set to 0 at the opening 

time. Regularly, as specified by the user (in the baseline, every 4 hours, according to the 2009 FDA Food 

Code), the FCS [in the baseline: the slicer(s), the generic FCS(s), the scale(s), and the utensil(s)], are 

washed and sanitized. The FCS clock is then reset.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

47 

At the end of each main event, the bacterial growth that occurred during that period is evaluated, and 

bacterial population numbers are updated, according to the growth models. 

Serve a customer

When this main event is chosen by the algorithm, the first action is the choice of the RTE product to be 

sold. The choice of a RTE product is proportional to the global sale of this RTE product, compared with

the other ones present in the retail deli. The mass sold is selected. Then the process varies if the RTE 

product is to be sliced (meat or cheese) or is to be served (deli salad).

Serving meat or cheese 

In Lubran et al.’s study of deli employee behavior [28], a regular baseline behavior sequence was 

commonly observed in employees serving customers in deli departments [28]. The food employee would

change gloves, open the deli case, pick up the chub, close the case, unwrap the chub, slice the RTE 

product onto his or her gloves, put the RTE product on a deli tissue, put the deli tissue on the scale, touch 

the scale, put the deli tissue in a plastic bag, put the label on the plastic bag, give the plastic bag to the 

consumer, rewrap the chub, open the case, put the chub in the case, close the case. Of course, some 

deviations from this baseline were observed. The frequencies of these alternatives were evaluated from 

the observational study and incorporated into the risk assessment model (Table 4). 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

48 

Table 4: Sequence of events when serving deli meat or deli cheese (derived from [28]) 

Event Number of times observed / total observations

Wipe the Slicer 7/83

Wash Hands and Change Gloves

OR Do not Wash Hands and Change Gloves

OR Do not Wash Hands and Do not Change Gloves

33/83

22/83

28/83

Touch a NFCS 4/83

Open the Case 68/83

Close the Case if had opened it

Touch the Refrigerator Handle 2/66

Open a New Chub if the mass of the chub < mass to be sold

No contact 

OR Contact New Chub - Sink

OR Contact New Chub – FCS

OR Contact New Chub - Slicer 

If open a new Chub

6/17

4/17

1/17

1/17

Pick up a Chub 83/83

Change Gloves 1/83

Touch the Knob of the Slicer 18/83

Slice onto Gloves

or Slice onto Deli Tissue

82/83

1/83

Touch the Scale 83/83

Contact Chub –- FCS 1/83

Open Case If had opened/closed it previously

Put Chub in Case 83/83

Close Case If had opened it previously

Wipe the Slicer if had not done it at the beginning 

15/68

Serving salad 

A similar baseline sequence was used for the main event “serve a deli salad.” The baseline sequence, as 

observed in retail delis [28], consists of change gloves, open the case, take salad bulk, close the case, pick 

up utensils, serve salad, put the RTE product on the scale, open the case, put salad bulk in case, close the 

case, touch the scale, wash utensils. Alternatives are provided in Table 5. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

49 

Table 5: Sequence of events when serving deli salad (derived from [28]). 

Event Frequency / Condition

Wash Hands and Change Gloves

or Do Not Wash Hands and Change Gloves

or Do Not Wash Hands and Do Not Change Gloves

1/11

6/11

4/11

Touch a NFCS 1/11

Open the Case

or Open the Case Twice

or Open the Refrigerator

9/11

1/11

1/11

Open a New Bulk of Salad if the remaining mass < mass to be sold

Pick up Salad Bulk 11/11

Pick up Utensils 11/11

Serve Salad 11/11

Close the Case If it had been opened

Touch the Scale 11/11

Wash and Sanitize the Utensil

or Sanitize the Utensil

or Do not Wash or Sanitize the Utensil

6/11

2/11

2/11

Customer Serving Times 

The duration of a serving (h) is assumed proportional to the serving size. Serving times were measured 

through buying individual sales of various deli RTE products at different retail delis across different times 

of the day. Weights and times are shown in Table 6. Times represent the time from when the order was 

placed until the chub was returned to the case.

Table 6. Observed data for calculation of customer serving time

RTE product Ordered weight Serving time

Pounds Grams minutes hours

American cheese 0.5 227 1.2 0.0200

Turkey 0.5 227 1.22 0.0203

Ham 0.5 227 3.03 0.0506

American cheese 1 454 2.33 0.0389

Turkey 1 454 2.67 0.0444

American cheese 1 454 4.27 0.0711

Turkey 1 454 3.02 0.0503

Ham 0.25 113 2.45 0.0408

American cheese 0.5 227 1.17 0.0194

A regression to predict time based on ordered weight was generated. Times were quite variable, 

depending on whether a new chub needed to be opened, the deli worker could easily find the chub 

ordered, etc. Consequently, the regression fit is rather poor. This high variability is captured by including 

a residual standard error from the regression in the time generation within the risk assessment model.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

50 

Time (hr) = 0.00007017 weight (g) + 0.01745 

R2

 = 0.30 

Residual standard error: 0.0155 

A minimum time of 0.02 hours (1.2 minutes) was assumed for any size serving. An example random 

draw of this approach is shown in the red symbols in Figure 9. The solid black circles represent the 

observed data.

Figure 9: Simulation of customer serving times

Thus, the customer serving time is modeled as 

duration = max(0.02, 0.01745 + 0.00007017 × SS + Normal(0, 0.0155)). 

where SS is the serving size (g).

The duration of a deli-salad serving is also assumed to be proportional to the serving size. It is assumed 

that the time a serving takes follows a normal distribution with a mean of 4 minutes and standard 

deviation of 0.4 minutes per pound, with a minimum of 30 s; that is, for a duration expressed in hours and 

a serving size in grams,

duration = max(0.0083, SS × Normal(0.0001468, 0.00001468)). 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

51 

6.3.1 Sites

In the baseline and all alternatives, no bacterial growth is considered on the sites. It is considered that the 

sites are not contaminated at the beginning of the simulation. However, the first 1% or 10,000 in 

1,000,000 servings are removed from the simulation prior to analysis (burn-in period). 

6.3.2 Basic processes

The main events are a single process or a succession of basic processes. Table 7 provides the 

correspondence between the main events used in the current model and the basic processes.

Table 7: Translation of the basic events in terms of basic processes

Basic event Basic process Objects involved

Remove Glove Remove all Bacteria Glove

Change Glove Cross contaminationa Glove – Hand

Put on Glove Changes Site for Hand/Glove 

Cross Contamination with 

Other Sites.

Glove

Close Case Cross Contamination Case – Hand or Gloveb

Open Case Cross Contamination Case – Hand or Glove

Open Chub with Contact Chub FCS Cross Contamination Chub – FCS

Open Chub with Contact Chub Sink Cross Contamination Chub – Sink

Open Chub with Contact Chub Slicer Cross Contamination Chub – Slicer

Pick-up Utensil Cross Contamination Utensil Handle – Hand or Glove

Put Chub on FCS Cross Contamination Chub – FCS

Serve Salad Cross Contamination 

Partitioning

RTE Product – Utensil

RTE Product – RTE Product Sold

Slice Slice Chub - RTE Product Sold– Slicer

Slice onto Glove Cross Contamination First Slice – Hand or Glove

Touch Knob Cross Contamination Slicer – Hand or Glove

Touch NFCS Cross Contamination NFCS – Hand or Glove

Touch Refrigerator Handle Cross Contamination Handle – Hand or Glove

Touch Scale Cross Contamination Scale – Hand Or Glove

Touch Scale Cross Contamination Scale – Hand or Glove

Wash Hands Inactivation/Removal (Wash) Hands

Wash Utensil Inactivation/Removal (Wash) Utensil and Utensil Handle

Wash and Sanitize Utensil Inactivation/Removal (Wash 

and Sanitize)

Utensil and Utensil Handle

Wipe Slicer Inactivation/Removal (Wipe) Slicer a

: “Cross contamination”:a: Possible cross contamination if one object carries some bacteria. b: “Hand or 

Glove”: Hand or glove according to the current hand status of the food employee. 

6.3.3 Temperature in display cases

Temperature of a RTE product is assumed equal to the temperature in the cases in which it is displayed.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

52 

Data

Temperature of the RTE products in the display cases were inferred from a study conducted by Ecosure 

[19]. The display case temperatures of a variety of RTE products were recorded in this study as follows: 

“When reaching a desired display case within their normal shopping pattern, some participants removed 

the RTE product to be purchased and inserted a thermometer directly into the RTE product. Participants 

left the thermometer in the RTE product until the temperature stabilized and then recorded the RTE 

product temperature and time of day” [19]. Among the tested RTE products, the data obtained for “Sliced 

meat” (Bologna) were used to derive a temperature distribution for deli cases in the model. Recorded 

temperatures ranged from -3.33°C (26°F) to 18.33°C (65°F). The raw data are reported in Table 8. 

Table 8: Raw storage temperature data for “Sliced Meat” (°F and °C.) 

°F 26 30 31 32 33 34 35 36 37 38 39 40 41 42

°C -3.33 -1.11 -0.56 0.00 0.56 1.11 1.67 2.22 2.78 3.33 3.89 4.44 5.00 5.56

n 1 2 1 9 8 11 11 23 23 68 45 120 51 61

°F 43 44 45 46 47 48 49 50 51 52 53 54 55 56

°C 6.11 6.67 7.22 7.78 8.33 8.89 9.44 10.00 10.56 11.11 11.67 12.22 12.78 13.33

n 25 64 54 47 30 73 22 63 10 20 7 9 6 7

°F 57 58 60 62 65

°C 13.89 14.44 15.56 16.67 18.33

n 1 5 10 2 2

(Source [19]) 

An analysis of the data leads to the conclusion that the collected data should be used directly, as an 

empirical distribution, rather than as a parametric one. 

Model 

In the risk assessment model, every morning the temperature of each display case is randomly sampled as 

being -1°F, +0°F, or +1°F as the day before, using an algorithm preserving the empirical distribution 

issued from the Ecosure dataset. For that purpose, a Metropolis-Hastings-like algorithm was used. Given 

Tn the current temperature (e.g., 40°F), Tn+1 the temperature of the following day, the algorithm is as 

follows: 

- draw a proposed new temperature Tprop that is equal to the next higher temperature observed in 

the dataset (e.g., 41°F) or equal to the next lower temperature observed in the dataset (e.g., 39°F) 

with equal probabilities; say that the draw is 41°F; 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

53 

- The number of observations of the temperature Tn in the dataset is En (e.g., 120), the number of 

observed temperature Tprop in the dataset is Eprop (e.g., 51). Calculate the ratio: r = Eprop / En; here: 

r = 51/120 = 0.425. 

- Accept the proposed move to Tprop with probability min(1, r). Here, draw a value uniformly 

between 0 and 1 and accept the proposal, if this value is lower than 0.425. If the proposal is not 

accepted, then Tn+1 = Tn = 40; else Tn+1 = Tprop =41.

It is possible to slow down the shift of temperature by moving only occasionally, using either a regular 

shift (example: one shift every week) or using a probability of proposal of shift that is independent of the 

current temperature. In the baseline model, a shift is proposed every day. 

Figure 10 illustrates the use of this method for the Ecosure data [19] as well as the concordance between 

the Ecosure empirical distribution and the simulated distribution.

Figure 10: Illustration of the Monte Carlo Markov Chain used to simulate the temperature changes in retail 

deli cases 

Left panel: time series for 1,000 days, Right Panel: empirical cumulative density function from Ecosure [19] and simulated 

empirical cumulative density function.

6.4. From the retail deli to foodborne illness

The output of the risk assessment model is a distribution of number of L. monocytogenes per serving of 

RTE products. L. monocytogenes growth may occur during the transport from the retail deli to the home 

and during the storage in the refrigerator at home. This growth will be a function of the RTE product 

characteristics and the time and temperature of storage. No cross contamination at home was considered. 

The final output is, then, the risk per serving of RTE food. This output is evaluated considering the 

consumption data and, eventually, the dose-response model.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

54 

6.4.1 Transport 

Time, temperature, and model 

The most pertinent datasets for time and temperature during transport are the Ecosure 2007 dataset [19]. 

The protocol within this study was as follows: when reaching a desired display case, participants recorded 

the temperature of the RTE food and the time of day. “Immediately before placing products in the home 

refrigerator or freezer, the temperature of each product was taken and the time recorded. The change in 

temperature from the retail case to home is considered to be short-term high-temperature abuse resulting 

from shopping, excessive ambient temperatures, and delays between removal of product from its display 

and re-refrigeration at home.” See details in [19] and corresponding data on www.FoodRisk.org. 

Time to reach home 

The best parametric distribution fitting the Ecosure data [19] on the duration between refrigerated storage 

at the retail deli and refrigerated storage at home ∆t (h) is a ln-normal distribution (Table 9), as compared 

to a normal, a Weibull, a gamma, a logistic, and a log-logistic distribution on the basis of the 

Anderson-Darling statistic (results not shown).

Table 9: Parameter of the fitted ln-normal distributions

Delay to get home (h) mean (s.e. Wald) 

[95% CI Bootstrap]

sd (s.e. Wald) 

[95% CI Bootstrap]

ρ spearman (p = 0)

Deli meat 0.132 (.0143) [.104, .159] .403 (.0101) [.384, .422] -.06 (p = .03)

Increase in temperature: Deli Meat

A linear model was developed using Ecosure data. In this model, the explained variable was the increase 

of temperature (∆T, °F) of the deli meat product. The duration of the transport (∆t, h), the weight of the 

product (w, oz.), and the initial temperature (T0, °F) were explaining variables. The residuals are 

important, and the adjusted R2 is only 0.09. Given these variables, the variation of temperature is 

eventually modeled as:

( )

( ) 19.5,0~

max 1276.27.13,0 0 2961.131.2 with

Normal

T T wt

ε

=∆ +×−∆×+×− ε
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

55 

Increase in temperature: Deli Salad 

A similar linear model using the variation in temperature as the explained variable and the temperature at 

t0, the transport duration and weight of the product, show that the weight is not a significant parameter for 

deli salad. This model leads to the following model for deli salad: 

( )

( ) 70.4,0~

max 1482.52.10,0 0 748.1 with

Normal

T T t

ε

=∆ +∆×+×− ε

No data are available for deli cheese; the model developed to predict the increase in temperature for deli 

meat was used for these cheeses.

Growth during transport 

The growth during transport of duration ∆t, starting at a temperature of T0 and ending at a temperature of 

Tf = T0 + ∆T, may be approximated by the growth that would occur during the same duration at a fixed 

temperature of T0 + ∆T/2. The growth models used are the same as those used in the deli department. 

Nevertheless, the growth occurs only when the temperature is higher than the minimal temperature of 

growth Tmin. A linear increase of the temperature during the transportation is assumed from T0, the 

temperature at retail to Tf, the temperature when the product arrives at home (≥ T0). T0 and Tf are possibly 

lower than Tmin. The mean temperature during which the growth occurs is then 

( ) ( )

2

max , max , min 0 min TTTT f m

+ = . 

The effective time of growth (time when T > Tmin) is 

. ( ( ) ( ))

( ) 0

min 0 min max , max ,

TT

TT TT

tt

f

f

eff −

− ×∆=∆

6.4.2 Home 

The time-temperature characteristics of home storage have been studied extensively by Kosa et al. [89]. 

Classical parametric survival modeling was used to derive parametric distributions from the RTI 

International storage practices dataset [90]. Time-temperature during home storage was modeled using 

the distributions proposed in Table 10 and Table 11. As an example for Deli Meat, the algorithm to draw 

a storage time at home is as follows (from Table 11): 

- draw a sample x ~ Uniform(0, 1) distribution 

- if x < 0.04 (i.e., 4%): the serving is eaten at the opening of the package. The time to consumption 

(days) is drawn from an exponential(1/0.457) distribution; 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

56 

- else, the serving is eaten at the last occasion. The time to consumption (days) is drawn from a 

Weibull(2.08, 8.33) distribution. 

Note that no data on hard cheese sliced to order are available. Soft cheese data were used instead. All 

simulated temperatures <0°C (32°F) were set to 0°C. The bacterial growth was modeled as detailed on

pages 35-42. 

Table 10: Fitted Laplace distribution of the refrigerator temperature data in Fahrenheit and Celsius 

Location Scale

Fahrenheit 39.3 4.23

Celsius 4.06 2.31

From [90] 

Table 11: Fitted distribution of time to first consumption of RTE food, using RTI International data

Time to first consumption (day) Ate package 

at one time: 

weighted %

Time to last consumption 

(day)

Fitted Distribution Fitted Distribution

Deli Meat – Sliced to Order Exponential(.457) 4% Weibull(2.08, 8.33) 

Soft Cheese Weibull(.873, 5.34) 8% Weibull(1.34, 18.7)

Deli Salad Exponential(.475) 14% Weibull(1.34, 7.49)

From [90]. Exponential(x) is the exponential distribution with mean x. 

6.4.3 Consumption

Specific consumption data were extracted from the 1999-2006 National Health and Nutrition Examination 

Survey (NHANES, a USDA/DHHS survey), using the FARETM Program (Food Analysis and Residue 

Evaluation Program, v. 8.63) developed by Exponent®. Details are given in Appendix 2. Figure 11 is the 

empirical cumulative distribution function of the serving size per eating occasion obtained from this 

analysis for deli salad, deli meat, and deli cheese. Table 12 provides some basic statistics from this 

distribution. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

57 

Figure 11: Distribution of serving size for deli salad (black), deli meat (blue) and deli cheese (red) for the total 

population

Table 12: Summary statistics of the empirical distribution of serving sizes, as simulated in the

L. monocytogenes retail model (g/eating occasions)

Deli Meat Deli Salad Deli Cheese

Minimum 1 1 1

1st Quartile 28 28 15

Median 51 69 24

Mean 63 99 31

3rd Quartile 83 138 39

Maximum 775 763 982

6.4.4 Dose-response model

The number of bacteria in the serving sold, after transport and growth in the refrigerator, is converted into 

a concentration using the mass of the serving sold. This concentration is then multiplied by the serving 

size (gram per serving) to obtain the ingested dose. Note that, at this level, the dose is not a discrete 

number but a continuous one, representing the mean of a Poisson distribution of the number of ingested 

cfu for the exponential dose-response [91]. 

The dose response model is a function that links the ingested dose to the probability of a given specified 

endpoint. A general review of the hazard characterization process may be obtained in FAO/WHO [92]. A 

specific review for the characterization of L. monocytogenes (i.e., characterization of severity and 

selection of appropriate biological end points to be modeled, factors that affect dose-response relations for

0 200 400 600 800

0.0 0.2 0.4 0.6 0.8 1.0

g

Fn(x)
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

58 

L. monocytogenes, approaches to modeling dose-response may be obtained in the FAO/WHO risk 

assessment of L. monocytogenes in RTE foods [59]. 

Relative to a “general” population, an increased susceptibility is commonly associated with:

- pregnant women and neonates, resulting in stillbirth or neonatal infection; 

- older adults; and 

- persons with particular conditions, including cancer and immunosuppressive therapy, AIDS, 

cardiovascular disease, congestive heart failure, diabetes, cirrhosis, and alcoholism [93, 94]. 

To date, two major dose-response models for humans scaled on epidemiological data are available: one 

developed within the 2003 FDA/FSIS quantitative assessment of relative risk to public health from 

foodborne L. monocytogenes among selected categories of RTE foods [3] and one developed within the 

FAO/WHO risk assessment of L. monocytogenes in RTE foods [59]. The second model uses the latter 

dose-response relationship. All details on the dose-response can then be obtained in the FAO/WHO risk 

assessment [59]. 

The FAO/WHO risk assessment considers invasive listeriosis as endpoint. It considers two 

subpopulations: the population with “increased susceptibility” [59] (including neonates, older adults and 

immunocompromised), and the population with “decreased susceptibility” [59] (all others). The model is 

an exponential dose-response model [91] that might be written as 

Pr(inf|D)=1-exp(-r D) 

where Pr(inf|D) is the marginal probability of invasive listeriosis in a population that ingests a food when 

the serving-to-serving variability of contamination follows a Poisson distribution of mean D. The 

exponential dose-response model is a single-hit model; it considers that pathogens act independently and 

that ≥1 pathogen is enough within the host to evoke the endpoint [91]. Parameter r, the unique parameter 

of this model, is the probability that one cell evokes the endpoint in a consumer at random from the 

reference population. It is considered in the exponential dose-response model that r is a constant for the 

specific population to which the model is applied. Note that the marginal dose-response relationships in 

the FAO/WHO [59] and the 2003 FDA/FSIS [3] risk assessment models closely compare, because they 

rely mostly on the same data. These models are almost linear at low-to-medium dose. 

Indeed, the unique r parameter per subpopulation of the FAO/WHO [59] model is estimated from 

exposure data issued from a draft FDA/FSIS report [95] and from the annual number of cases of 

listeriosis estimated in the U.S. [96]. The point estimates for r used in this model are 1.06 × 10-12 for the 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

59 

susceptible population and 2.37 × 10-14 for the other population [see Table 2.17, p. 56 and Table 2.20, 

p.58, 59]. 

Uncertainty for the r parameters was derived based on four parameters that influence the dose-response 

relations; namely the percentage of the population in the U.S. with increased susceptibility to

L. monocytogenes, the percentage of cases of total severe listeriosis cases associated with the increasedsusceptibility population in the U.S., the total number of cases in the U.S., and the maximum achievable 

dose of L. monocytogenes per serving [59]. Using the FAO/WHO [59] assumptions, a Monte Carlo 

simulation was built to derive an empirical distribution of uncertainty for each of the two r parameters. A 

median r estimate is 7.76 × 10-13 [1.32 × 10-13; 6.98 × 10-12] for the susceptible population and 1.76 × 10-14

for a 95% CI of [2.07 × 10-15; 2.10 × 10-13] for the other population. Note the scale of the uncertainty. The 

fraction of the population in the two subpopulations also is uncertain. No uncertainty is included for any 

dose-response parameter for this risk assessment, because the emphasis is on comparing outputs for 

different deli operating rules. Because this confounding uncertainty is not the purpose of this study, it was 

chosen to use the point estimates for r. 

6.5. Additional baseline inputs

6.5.1 Retail deli characteristics

Two categories of retail deli are currently considered: a retail deli A type (20% of simulated retail delis) 

and retail deli B type (80% of simulated retail delis). Retail deli A type includes one of each considered 

category of object, (i.e., one Floor, one Sink, one Refrigerator [handle], one Scale, one Case, one Utensil 

(and its handle), one Slicer, one Food-Contact Surface, and one Non-Food-Contact Surface). The retail 

deli B type includes two of each considered category of object. Each retail deli is opened 14 hours per 

day. 

In the baseline risk assessment model, food-contact surfaces are washed and sanitized once every 4 hours 

of operation. When food workers engage in sporadic cleaning, it is assumed that they wash four objects. 

Also, it is considered that food workers wear gloves while serving customers 100% of the time (as 

observed by Lubran et al. [28] in States with the glove policy). 

In the baseline, it is assumed that one slicer is used only for deli meat and that the other slicer is used for 

deli cheese in larger retail delis with two slicers. There is no separation of deli cases or any other type of 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

60 

objects according to the type of RTE food (e.g., cheese, deli salad, deli meat). When multiple sites can be 

used in the retail deli, the specific sites (e.g., slicer, utensil) used for that serving are randomly picked. 

6.5.1 General operation

In all baselines and scenarios, the probability of occurrence of a Non Deli Time event among the main 

events (“Non Deli Time,” “Sporadic Clean,” and “Serve Customer”) is 30%. Its duration follows a normal 

distribution with mean 3 minutes and standard deviation 0.3 minute. The probability of occurrence of a 

Sporadic Clean event among the main events is 5%. Its duration follows a normal distribution with mean 

10 minutes and standard deviation 1 minute. The main event “Serve a Customer” occurs with a 

probability of 65%. 

6.5.2 RTE products

A retail deli from the baseline model includes 20 different RTE products. The sales characteristics are 

presented in Table 14. The relative frequency of sales for most of the RTE products are from “What's in 

store 2010” [97]. The sizes of the servings sold are sampled from the empirical distribution observed by 

Ecosure [19] from sales of 787 various deli meats. The empirical cumulative distribution is provided in

Figure 12. 

Figure 12: Empirical cumulative distribution of the size of RTE food serving in a retail deli

(Source: [19]) 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

61 

It is important to note that the initial concentration distribution (cfu/gram) for all RTE products is 

assumed to be a log10 normal distribution. The mean and standard deviation of this log10 normal 

distribution are assumed to be issued from a multinormal distribution with mean (-9.228, 2.923) and 

covariance matrix 















−

− = 032.0085.0085.0232.0

C . These parameters are maximum likelihood estimates from 

the FSIS L. monocytogenes verification sampling program data [8, 98]. Data from 2006–2010 were used 

to estimate this distribution [99]. The dataset consisted of 56,985 samples, with an observed prevalence of 

0.42% (239 positives). Twenty-two of the samples had quantifiable concentrations above the detection 

limit. The highest observed concentration was 230 MPN/g. A maximum likelihood estimation algorithm, 

which accounted for the censoring of the data due to the multiple detection limits [16], was used to fit the 

lognormal model to the data. To avoid any unrealistic concentration using this unbounded, heavily tailed, 

log10 normal distribution, it was truncated to 500 cfu per gram 

For a 2,270 gram chub, and assuming a Poisson-lognormal(-9.228, 2.923) distribution of the bacteria, this 

distribution leads to a prevalence (percentage of chubs containing >0 bacteria) of 2.97%. Table 13

presents some characteristics of this distribution and of distributions that were used in some of the 

alternatives. 

Table 13: Characteristics of the distribution of bacteria in contaminated chubs (2,270 grams) according to the 

mean of the log10 normal-Poisson distribution 

Mean sd prevalence 

(prob > 0 cfu)

prob > 2270 cfu

(1 cfu/g)

prob > 22,700 cfu

(100 cfu/g)

Baseline -9.228 2.923 2.97% 0.08% 0.01%

Alternatives -9.529 2.923 2.35% 0.06% 0.00%

-8.928 2.923 3.71% 0.11% 0.01%

-7.000 2.923 12.71% 0.83% 0.10%

-5.000 2.923 31.94% 4.36% 0.83%

-3.000 2.923 58.04% 15.24% 4.36%

The characteristics of the various RTE products are reported in Table 15. These characteristics are 

extracted from the chemical analysis results provided in the predictive microbiology literature [72, 77, 79, 

80, 82, 100-120]. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

62 

Table 14: Sales and characteristics of the RTE products

RTE Product Type (Example*) Number of 

Chub/Bulk in 

each Retail Deli

Sales quantity 

(Relative 

Quantity)

Mass of a 

Chub/Bulk 

in g.

(Sd)

Deli Meat (Uncured Ham) 2 4.7 2724 (227)

Deli Meat Low Growth (Cured Ham) 2 4.7 2724 (227)

Deli Meat No Growth (Cured Ham w GI) 1 4.7 2724 (227)

Deli Meat (Uncured Turkey) 2 5 2724 (227)

Deli Meat Low Growth (Cured Turkey) 2 5 2724 (227)

Deli Meat No Growth (Cured Turkey w GI) 1 5 2724 (227)

Deli Meat (Uncured Bologna) 1 1 2724 (227)

Deli Meat Low Growth (Cured Bologna) 1 1 2724 (227)

Deli Meat No Growth (Cured Bologna w GI) 1 1 2724 (227)

Deli Meat No Growth (Pepperoni) 1 1 2724 (227)

Deli Meat No Growth (Salami) 1 3 2724 (227)

Deli Cheese Low Growth (Colby) 1 1 3178 (227)

Deli Cheese No Growth (Monterey Jack) 1 1.4 3632 (227)

Deli Cheese No Growth (American) 1 7.6 3632 (227)

Deli Cheese No Growth (Provolone) 1 1.4 3632 (227)

Deli Cheese Low Growth (Swiss) 1 1.4 3632 (227)

Deli Salad (Potato) 1 5 4540 (227)

Deli Salad Low Growth (Potato w GI) 1 5 4540 (227)

Deli Salad (Protein) 1 2 4540 (227)

Deli Salad Low Growth (Protein w GI) 1 3 4540 (227)

* Examples are proposed for illustrative purposes

Table 15: Characteristics of the RTE products

RTE Product Type (Example*) pH aw Nitrites 

(ppm)

Potassium 

Lactate 

(w/w %)

Sodium 

Diacetate 

(w/w %)

Deli Meat (Uncured Ham) 6.4 0.97 0 0 0

Deli Meat Low Growth (Cured Ham) 6.4 0.97 150 0 0

Deli Meat No Growth (Cured Ham w GI) 6.4 0.97 150 1.65 0.12

Deli Meat (Uncured Turkey) 6.3 0.96 0 0 0

Deli Meat Low Growth (Cured Turkey) 6.3 0.96 150 0 0

Deli Meat No Growth (Cured Turkey w GI) 6.3 0.96 150 1.65 0.12

Deli Meat (Uncured Bologna) 6.3 0.93 0 0 0

Deli Meat Low Growth (Cured Bologna) 6.3 0.93 150 0 0

Deli Meat No Growth (Cured Bologna w GI) 6.3 0.93 150 1.65 0.12

Deli Meat No Growth (Pepperoni) 4.67 0.83 0 0 0

Deli Meat No Growth (Salami) 5 0.91 0 0 0

Deli Cheese Low Growth (Colby) 5.2 0.95 0 0 0

Deli Cheese No Growth (Monterey Jack) 5.25 0.93 0 0 0

Deli Cheese No Growth (American) 5.6 0.92 0 0 0

Deli Cheese No Growth (Provolone) 5.2 0.91 0 0 0

Deli Cheese Low Growth (Swiss) 5.2 0.92 0 0 0

Deli Salad (Potato) 4.6 0.998 0 0 0

Deli Salad Low Growth (Potato w GI) 4.6 0.998 0 1.65 0.12

Deli Salad (Protein) 5 0.988 0 0 0

Deli Salad Low Growth (Protein w GI) 5 0.988 0 1.65 0.12

* Examples are proposed as illustrative purposes

The FDA Food Code [27] specifies that RTE, potentially hazardous food prepared and held refrigerated 

for more than 24 hours in a food establishment must be marked at the time the original container or 

package is opened in a food establishment, to indicate the date by which the food shall be consumed or 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

63 

discarded. In the baseline model, chubs of ham, turkey, bologna, and salads are discarded if they are not 

used 7 days after the date they are opened. Foods that do not require a 7-day limit after opening, 

according to the FDA Food Code [27] [e.g., pepperoni, salami, and cheese (Colby, Monterey Jack, 

American, Provolone, Swiss)] are discarded after 30 days, if they are not used. 

In the baseline model, RTE products are not pre-sliced; they are sliced and served at the consumer’s

request. 

6.5.3 Transfer coefficients

The current values of the parameters defining the transfer coefficient distributions (MTij and STij ;see 

section 6.1.1), following the analysis described in Hoelzer et al. [37], are reported in Table 16. The 

parameters are shown only for the transfers that are currently simulated in the model. See Hoelzer et al. 

[37] for details. 

Table 16: Mean (standard deviation) of the log10 of the transfer coefficients for L. monocytogenes at retail

To 

Meat

Cheese Salad Floor Sink Handle Case Utensil Utensil 

Handle

Slicer Scale FCS NFCS Glove Hand

From

Meat

-0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-1.69

(0.81)

-1.69

(0.81)

Deli 

Cheese

-0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-1.69

(0.81)

-4.96

(0.37)

Salad -0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-1.69

(0.81)

-4.96

(0.37)

Floor -1.84

(0.87)

-1.84

(0.87)

Sink -0.28

(0.2)

-0.28 

(0.2) 

-1.84

(0.87)

-1.84

(0.87)

Handle -1.84

(0.87)

-1.84

(0.87)

Case -1.84

(0.87)

-1.84

(0.87)

Utensil -0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-1.84

(0.87)

-1.84

(0.87)

Utensil 

Handle

-1.84

(0.87)

-1.84

(0.87)

Slicer -0.28

(0.2)

-0.28 

(0.2) 

-1.84 

(0.87) 

-1.84

(0.87)

Scale -1.84

(0.87)

-1.84

(0.87)

FCS -0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-1.84

(0.87)

-1.84

(0.87)

NFCS -0.28

(0.2)

-0.28

(0.2)

-0.28

(0.2)

-1.84

(0.87)

-1.84

(0.87)

Glove -4.96

(0.37)

-4.96

(0.37)

-4.96

(0.37)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-3.43

(0.79)

-3.43

(0.79)

Hand -1.69

(0.81)

-4.96

(0.37)

-4.96

(0.37)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-1.84

(0.87)

-3.43

(0.79)

-3.43

(0.79)
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

64 

6.6. Implementation

The model is written in open-source language R version > 2.11.1 [121], which is free and available for 

download at http://www.r-project.org/. The parameters are specified in a Microsoft® Excel workbook read 

by R through an ODBC (Open Database Connectivity)(RODBC package).

The major disadvantage of discrete-event models is that their computing times are long. Any state being 

dependent on the preceding one, this prevents vectorization and requires loops in R, which run much 

slower. A lot of effort has been made while writing the code in order to i) be able to launch the code on 

parallelized processors, using the R SNOW package; ii) profile the time of computing. 

The Interagency Retail L. monocytogenes Risk Assessment Workgroup has access to High Performance 

Computing (HPC) tools brought by the Office of Science and Engineering Laboratories, Center for 

Devices and Radiological Health, FDA. This allowed the group to run the model on up to 2,016 cores 

(Figure 13). Running the code in parallel greatly reduced run time and was used to provide results. 

CSV Outputs

R model

“Blue Meadow” cluster

2,016 cores, 21 TFlops

Brought by the Office of Science and Engineering Laboratories, 

CDRH - FDA 

Parallel computing

Parameters

Excel Data File

Figure 13: General scheme of simulations

The current general algorithm is as provided in Figure 13. On 101 cores, it takes about 0.45 hour to run 

100 retail delis in parallel; 1,100,000 sales for each retail deli.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

65 

6.7. Studying the model 

6.7.1 RTE products

Given that each simulation is run for periods measured in years (1,000,000 servings) for 100 retail delis, a 

resulting output file size that would record each sale would be too large to be handled. Thus, the risk 

assessment model outputs include summary statistics of the servings and events for each simulation. The 

summary for the serving includes, for each retail deli and food sub category: 

- number of servings; 

- number of contaminated servings; 

- mean number of L. monocytogenes cfu among positive RTE product servings; 

- mean number of L. monocytogenes cfu per gram of RTE product among positive servings; 

- mean L. monocytogenes concentration among positive RTE product servings when sold and when 

eaten;

- mean ingested dose of L. monocytogenes; 

- mean risk of invasive listeriosis in the two subpopulations; 

- mean of the log10 of these outputs; 

- sum of square of these outputs.

These few statistics are sufficient to build various other statistics, such as the mean for all servings 

(negative and positive), the variance, the standard error, within sub-category of RTE food, within 

uncertainty loop, within repetition, etc. It also is sufficient to build an analysis of variance. 

Moreover, in order to better characterize the cumulative density function of these outputs, another 

summary provides the number of contaminated servings that falls in some contamination level bins. 

These statistics count the number of deli sales that fall in the (-∞; 0.001), [0.001; 0.01), [0.01; 0.1),... 

[1E6, 1E7), [1E7, ∞) bin of contamination (in cfu/g of RTE products). Knowing these counts, it is easy to 

rebuild the interval-based cumulative density function within sub-category of food, within repetition, etc. 

If needed, a file containing a record for each of the simulated servings may be built. It includes all 

characteristics of the product sold (e.g., growth characteristics, final size, associated risk). This file is 

usually too large to be handled, but may be built and studied for shorter runs, to study a very specific 

situation. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

66 

6.7.2 Sites

A file is provided to track the contamination of the sites: the summary reports for each retail deli and for 

each site, the kind of retail deli (e.g., “A” or “ B”), the fraction of time a site is contaminated, and the 

mean number of events during which the given site stays contaminated. 

The model also can output the time series of L. monocytogenes counts at each site each time an event 

occurs or for one selected type of event. This file is usually too large to be handled for a complete run. 

6.7.3 Transfer matrix

Within the risk assessment model, a matrix was developed to track each transfer of L. monocytogenes

during a simulation. The structure of the matrix is depicted in Figure 14. 

Figure 14: Structure of the L. monocytogenes transfer matrix in the model of the Interagency Risk 

Assessment: L. monocytogenes in Retail Delicatessens

The risk assessment model summarizes the information from a run by proving i) the number of contacts 

between objects, ii) the number of effective contacts (i.e., number of contacts with at least one cfu 

involved), iii) the average number of L. monocytogenes cfu transferred per contact, and iv) the overall 

number of transferred L. monocytogenes cfu. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

67 

The intensity of the transfers occurring in a model may be graphically illustrated as in Figure 20, to study 

the dynamics of the transfer of L. monocytogenes in the risk assessment model as well as the impact of 

changes in deli practices on each L. monocytogenes transfer.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

68 

7. Risk Assessment Results and Discussion

7.1. Risk management questions and model approaches 

7.1.1 Baseline retail deli and RTE product conditions 

The model requires the input of bacterial loadings and frequencies of bacterial transfer from the niches 

and the mean bacterial log10 concentration in the contaminated product type. Baseline conditions had to be 

established to evaluate the public health impact of changes in retail deli practices. The conditions in 

different retail stores and within a single retail deli at different times may vary a great deal in terms of

L. monocytogenes levels of environmental and product contamination. A sensitivity analysis was 

conducted, in section 7.2.1, of the levels and frequencies of L. monocytogenes contamination from niches 

and the mean levels of L. monocytogenes on RTE products entering the retail deli for further preparation 

(e.g., slicing) before being sold to the consumer. 

The approach used in this risk assessment was to evaluate the public health effect of various under six 

different baseline conditions that may characterize a retail deli and the RTE product it serves at different 

times over the course of operations. These six baseline conditions are: 

• A retail deli with multiple niches that release L. monocytogenes to food-contact surfaces. This 

approach would also represent retail delis where general environmental contamination of 

non-food-contact surfaces is transferred to surfaces that may be in contact with food. This 

baseline assumes that, on average, 100 L. monocytogenes are released to food-contact surfaces 

periodically, with an average period of one week (W) between two releases. The level selected 

for this specific baseline was selected among other levels (see section ). This baseline was

denoted Multiple Niche 100W (1st baseline model condition). 

• A retail deli with no niches or environmental L. monocytogenes transfer. This baseline was

denoted No niche (2nd baseline model condition). 

• A retail deli with no niche or environmental L. monocytogenes transfer, with one incoming RTE 

product contaminated at levels higher than those of other products in the deli (mean of the log10: -

5 log10 cfu/g vs. -9.2 log10 cfu/g) [

7.2.1

]. These baselines assume a mean of the log10 normal 

distribution of the concentration in that incoming RTE product of -5 log10 cfu/g, higher than the 

baseline (-9.2 log10 cfu/g). The L. monocytogenes contamination level selected for these specific 

baselines were selected to generate a readily observable increase in the predicted public health 

risk and were further evaluated by a sensitivity analysis (see section 

99

). Two types of retail 

deli situations are examined, including:

7.2.1
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

69 

o The incoming contaminated RTE product supports growth. (Sales volume and other RTE 

product-specific data based on available data on RTE food that support L. monocytogenes

growth). This baseline was denoted Incoming Growth Chub (3rd baseline model 

condition). 

o The incoming RTE product does not support growth. (Sales and other RTE productspecific data were based on available data on RTE food that does not support

L. monocytogenes growth). This baseline was denoted Incoming Non-Growth Chub (4th

baseline model condition). 

• A retail deli compliant with the 2009 FDA Food Code guidance to maintain deli cases at ≤41°F 

(≤5 °C). 

o A retail deli with multiple niches and compliant temperature control. This baseline was

denoted Niche & Temperature Control (5th baseline model condition).

o A retail deli without any niches with compliant temperature control. This baseline was

denoted Temperature Control (6th baseline model condition). 

When evaluating the impact of incoming contaminated product, sales and product composition were taken 

from typical high-sale deli meat and cheese food products. The incoming concentrations were selected as 

a range to be higher than actually observed, so that the potential public health impact of these higher 

levels could be evaluated.

In the absence of ad-hoc data, the specific values defining each baseline type are merely representative. 

For example, the Multiple Niche 100W baseline considers that niches transfer 100 cfu on an average 

weekly frequency. The baseline values are thus representative of a type of retail delis. A range of values 

for niche characteristics and levels of contamination of incoming products are evaluated in a sensitivity 

analysis in Section 7.2.1. 

7.1.2 Scenarios: Changes in retail deli practices

Various scenarios were evaluated to inform the specific risk management questions posed for this risk 

assessment (see Section 3). The food safety intervention scenarios that were run for each of the baseline 

conditions are given below. The abbreviations used on the graphs are shown first in bold, followed by a 

brief explanation. The reader can refer to a list of these abbreviations in the Table on page vi. These 

scenarios are grouped according to the risk management question the scenario illustrates. 

Because of the large number of scenarios considered in this risk assessment, they were divided into two 

categories: 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

70 

• Category 1: scenarios primarily based on sanitation. 

• Category 2: worker/industry behavior and scenarios primarily directed at impacting

growth through time and temperature control. 

Risk assessment model scenario analyses were conducted to inform specific risk management questions 

as follows:

1) What would be the potential public health impact of practicing more frequent or more extensive 

cleaning procedures for FCS and/or NFCS than is currently specified in the 2009 FDA Food Code [27] on 

(1) the prevalence of L. monocytogenes in RTE products sold in retail delis and (2) the corresponding 

mean risk of invasive listeriosis? The corresponding tested scenarios were:

• Wash & Sanitize: Increase the effectiveness of retail deli cleaning from simply washing to 

washing and sanitizing [i.e., from an average log10 reduction obtained from a Pert(-1.5, -0.5, 0) to 

a Pert(-8, -6, -1.5)]. 

• Clean 8 Sporadic: double the number of retail deli sites sporadically cleaned from 4 to 8; 

• No Sanitation: do not conduct any wiping, washing, or sanitizing of retail deli FCS;

• No Sporadic Cleaning: retail deli workers clean FCS as required by the 2009 FDA Food Code, 

but do not conduct any additional sporadic cleanings, as observed by Lubran et al. [28]. 

2) What would be the potential public health impact of increasing the use of single-service gloves in retail 

delis?

• No Glove: retail deli workers do not use gloves when serving customers;

• Gloves Every Serving: retail deli workers change gloves for every sale of RTE products.

3) What if scale touch pads, refrigerator and deli case handles, and other frequently touched NFCS were 

considered food-contact surfaces and were therefore cleaned and sanitized at a minimum frequency, per 

FDA Food Code [27] requirements? 

• NFCS as FCS: retail deli workers clean deli NFCS as if they were FCS (i.e., every 4 hours, in 

accordance with the 2009 FDA Food Code). 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

71 

4) What if practices were in place so that no cross contamination occurred in delis (i.e., no further 

L. monocytogenes added to incoming RTE products)?

• Transfers to 0: scenario in which L. monocytogenes cross contamination in the retail deli would 

result only from the deli slicer (i.e., set cross contamination transfer coefficients to 0 for all sites 

except the slicer);

• Transfers and Slicer to 0: scenario in which there is no L. monocytogenes cross contamination 

in the retail deli (i.e., set cross contamination transfer coefficients to 0, meaning no cross 

contamination occurs for all sites, including the slicer). 

5) What if display cases were not touched with gloved or bare hands (i.e., use of tissues or automatic door 

open/shut)? 

• No-Contact Glove Case: retail deli workers do not use their hands (gloved or ungloved) to open 

the deli case (e.g., if a floor switch is used).

6) What would be the potential public health impact if the level of L. monocytogenes contamination were

reduced in RTE foods coming into the retail deli? 

• Reduce Level: lower the mean incoming L. monocytogenes concentration on all RTE products 

from a mean of the log10 of -9.2 to a mean of the log10 of -9.5 (see Section 7.2.1). This leads to an 

average prevalence for a 2,270 g chub of 2.35% vs. 2.97% in the baseline.

7) What would be the potential public health impact of “pre-slicing” all RTE products vs. “slicing to 

order” (hypothesis: less cross contamination occurs in morning, prior to other cross contamination 

events).

• Pre-slice: retail deli workers pre-slice all chubs of RTE product (deli meat and deli cheese) in the 

morning, after cleaning. A quantity equal to the median of the daily sales is pre-sliced every 

morning. When a consumer orders a RTE product, the food worker serves the pre-sliced RTE 

product, until all of the pre-sliced quantity is sold. If needed, additional RTE product is sliced to 

order. At the end of the day, the remaining pre-sliced RTE product is discarded.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

72 

8) What would be the potential public health impact of using separate slicers and/or separate counters for 

RTE products that permit growth of L. monocytogenes and for RTE products that do not? 

• Separate Slicer: retail deli workers use a separate slicer for RTE products that support growth of

L. monocytogenes versus RTE products that do not; 

• Separate Slicer Case: retail deli workers use a separate slicer and a separate deli case for RTE 

products that support the growth of L. monocytogenes versus RTE products that do not. 

9) What would be the potential public health impact of lowering the level of environmental contamination 

of food-contact surfaces?

• Lower Env Contam: reduce transfer of L. monocytogenes among RTE products, FCS, and 

NFCS (i.e., reduce transfer coefficients by 50%) in the retail deli.

10) What if food workers did not slice RTE products directly on their gloved hands? 

• Do Not Slice Onto Gloves: during the observational study, it was observed that the food worker 

usually gets the slices on his or her gloves before putting the slices on the deli tissue (rather than 

slicing directly onto the deli tissue). In this alternative, retail deli workers collect the sliced RTE 

products directly on tissue paper, rather than on their gloves.

11) What is the potential public health impact of bacterial growth in retail delis?

• Temp = 5°C: set the retail deli case temperature for all retail delis to 5°C (41°F) (i.e., in 

compliance with the 2009 FDA Food Code), rather than using real-world deli case temperatures 

reported by Ecosure [19]; 

• No Growth (T = -5°C): set all retail deli case temperatures to -5°C (23°F). At this temperature, 

no L. monocytogenes growth will occur. 

12) What would be the potential public health impact of complete compliance to the cold-holding 

requirements for certain RTE foods in deli cases (hold at ≤41°F i.e., ≤5 °C)? 

• Temp ≤ 5°: use the retail deli case temperatures observed in the Ecosure dataset [19] at or below 

5°C (41°F). This implies that all retail delis with deli case temperatures exceeding the 2009 FDA 

Food Code recommendation come into compliance. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

73 

13) What would be the impact of shortening the time a RTE product can be used in a deli department? 

• Shorten time in retail deli: retail delis reduce the length of time RTE products are held before 

they are sold or disposed from 7 to 4 days.

14) What would be the impact if all (or no) RTE products (e.g., RTE meat and poultry products, RTE deli 

salads) coming into the deli were formulated with growth inhibitors? 

• All GI: reformulate all RTE products sold at the retail deli that would otherwise support 

L. monocytogenes growth to include growth inhibitors, to restrict growth [same growth inhibitor 

(GI) formulation as cured ham with GI]; 

• No GI: reformulate all RTE products that support L. monocytogenes growth that are sold at the 

retail deli and do not include GI to restrict L. monocytogenes growth. 

7.2. Baseline analysis

This section provides the results of the various baselines and compares the various scenarios within each 

baseline. The following section (7.3) compares the scenarios across the various baselines. 

The actual levels selected for specific baselines (“Retail deli with Multiple Niches” and “Retail delis with 

contaminated incoming RTE products”) were selected to be large enough to show an increased predicted 

risk relative to the “no niche” baseline. Before providing the results of the various baselines, Section 7.2.1

provides a sensitivity analysis of the levels of specific baselines.

7.2.1 Sensitivity analysis 

Figure 15 shows a sensitivity analysis of the mean predicted risk of listeriosis per serving of RTE food for 

a susceptible population [59] as these choices change. The blue bars to the left are for different niche 

loadings. “W” and “D” stand for mean weekly or mean daily transfers, respectively. The number 

represents the mean number of cfu transferred to the site when transfer occurs. For contaminated RTE 

product, the number in parentheses represents the mean log10 concentration [e.g., the baselines scenarios 

“Retail delis with contaminated incoming RTE product” are from the (-5) bars]. 

It is important to note that, unlike the results presented for incoming contaminated RTE products’ 

baselines (see Sections page 88 and page 92), the estimated risk presented here specifically excludes sales 

of the contaminated RTE product itself. Thus, any increase in predicted public health risk is due only to 

consumption of RTE foods cross contaminated at the retail deli. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

74 

Scenarios

Baseline

Niche Slicer 100-W

Niche Slicer 1000W

Niche Slicer 100D

Multiple Niches 100W

Multiple Niches 1000W

Multiple Niches 100D

IncLm growth (-9.2) IncLm growth (-8.9) 

IncLm growth (-7) IncLm growth (-5) IncLm growth (-3) 

IncLm no growth (-9.2) IncLm no growth (-8.9) 

IncLm no growth (-7) IncLm no growth (-5) IncLm no growth (-3) 

IncLm Salad (-5)

Mean Susceptible Risk per Serving

0.0

1.0e-7

2.0e-7

3.0e-7

4.0e-7

5.0e-7

6.0e-7

7.0e-7

Niche Contaminated Product

Figure 15. Sensitivity analysis for niches and contaminated RTE product

Baseline: Retail deli with no niche or environmental bacteria transfer on food-contact surfaces.

Niche Slicer 100-W: Retail deli with transfers of 100 L. monocytogenes cfu on the food-contact surface of the 

slicer, with an average frequency of one transfer per week.

Niche Slicer 1000-W: Retail deli with transfers of 1,000 cfu L. monocytogenes on the food-contact surface of the 

slicer, with an average frequency of one transfer to RTE product per week.

Niche Slicer 100-D: Retail deli with transfers of 100 cfu L. monocytogenes on the food-contact surface of the slicer, 

with an average frequency of one transfer to RTE product per day.

Multiple Niche 100-W: Retail deli with transfers of 100 cfu L. monocytogenes on multiple food-contact surfaces, 

with an average frequency of one transfer to RTE product per week.

Multiple Niche 1000-W: Retail deli with transfers of 1,000 cfu L. monocytogenes on multiple food-contact 

surfaces, with an average frequency of one transfer to RTE product per week.

Multiple Niche 100-D: Retail deli with transfers of 100 cfu L. monocytogenes on multiple food-contact surfaces, 

with an average frequency of one transfer to RTE product per day.

IncLm growth (-9.2): Retail deli with no niche or environmental bacteria transfer on food-contact surfaces, with 

L. monocytogenes average contamination of incoming RTE products equal to the current estimate of 

federally inspected plant (mean of the log10 concentration: -9.2 log10 cfu/g). 

IncLm growth (-8.9): Retail deli with L. monocytogenes average log10 contamination of incoming RTE products 

that support growth of L. monocytogenes equal to -8.9 log10 cfu/g, other RTE products having an average 

log10 contamination equal to -9.2 log10 cfu/g.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

75 

IncLm growth (-5): Retail deli with L. monocytogenes average log10 contamination of incoming RTE products that 

support growth of L. monocytogenes equal to -5 log10 cfu/g, other RTE products having an average log10

contamination equal to -9.2 log10 cfu/g.

IncLm growth (-3): Retail deli with L. monocytogenes average log10 contamination of incoming RTE products that 

support growth of L. monocytogenes equal to -8.9 log10 cfu/g, other RTE products having an average log10

contamination equal to -3 log10 cfu/g.

IncLm no growth (-9.2): Retail deli with no niche or environmental bacteria transfer on food-contact surfaces with 

L. monocytogenes average contamination of incoming RTE products equal to the current estimate of 

federally inspected plants (-9.2 log10 cfu/g). Same situation as in S-IncLm growth (-9.2).

IncLm no growth (-8.9): Retail deli with L. monocytogenes average log10 contamination of incoming RTE products 

that do not support growth of L. monocytogenes equal to -8.9 log10 cfu/g, other RTE products having an 

average log10 contamination equal to -9.2 log10 cfu/g.

IncLm no growth (-5): Retail deli with L. monocytogenes average log10 contamination of incoming RTE products 

that do not support growth of L. monocytogenes equal to -5 log10 cfu/g, other RTE products having an 

average log10 contamination equal to -9.2 log10 cfu/g.

IncLm no growth (-3): Retail deli with L. monocytogenes average log10 contamination of incoming RTE products 

that do not support growth of L. monocytogenes equal to -8.9 log10 cfu/g, other RTE products having an 

average log10 contamination equal to -3 log10 cfu/g.

Note: For the specific objective of this sensitivity analysis, the estimated risks for the simulations that incorporated a 

contaminated RTE product exclude the sales of the contaminated product itself. These results should not be 

compared to those presented in the later sections. For example, if the mean incoming log10 level is increased to -5 

log10 cfu/g, the mean risk per serving for the susceptible population with an incoming product that supports growth 

is estimated to be 16.6 × 10-7 when the sales of the contaminated RTE product are considered. If the contaminated 

product sales are excluded, the mean risk is 2.9 × 10-7 as shown in the graph. These figures are 2.8 × 10-7 vs. 

2.3 × 10-7 when the incoming contaminated product does not support growth.

The main conclusions of the sensitivity analysis are that:

• allowing more L. monocytogenes into the retail deli environment increases the predicted risk, 

regardless of whether these bacteria come from a niche(s) in the retail deli environment or from 

L. monocytogenes on RTE product from the processor; 

• highly contaminated RTE product cross contaminates other RTE products, leading to an increase

in predicted risk per serving from consumption of the cross contaminated products. This is 

especially true for highly contaminated RTE products that permit growth, but it is also true for 

those that do not permit growth. 

• allowing more frequent environmental cross contamination (daily vs. weekly) has proportionally 

more impact than allowing more bacteria per cross contamination event (100 vs. 1,000 cfu per 

contamination event); 

Based on this sensitivity analysis, the following baselines were chosen: 

• Retail deli with Multiple Niches/Transfers from the Environment : multiple niches/transfers, 100 

cfu, with an average frequency of one transfer per week;
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

76 

• Highly contaminated RTE products: mean level of L. monocytogenes log10 contamination of -5 

log10 cfu/g.

7.2.2 Baseline conditions

Baseline Condition: Multiple Niches / Transfers from the Environment 

Note that the scales are not held constant across each graph in the following sections, notably for different 

baselines. The baselines and changes in retail deli practices are identified using an abbreviation, as 

specified above. The reader can refer to the table on page vi for an extended description of these 

abbreviations. 

For this baseline, a retail deli with multiple niches is used. Each niche contaminates its associated site 

with L. monocytogenes at a mean frequency of once per week and at a mean transfer of 100 cfu for each 

contamination event. This baseline would also mimic retail delis with frequent transfers from the retail 

deli environment. 

The change in RTE products contaminated with L. monocytogenes and corresponding change in predicted 

public health risk (estimated mean risk per serving to the susceptible population) as a result of changes in

sanitation and retail deli worker/industry behavior are shown in Figure 16 and Figure 17. The bottom 

graphs show the prevalence of L. monocytogenes in RTE products and the estimated risk of listeriosis. 

The top graphs present the relative change (%) in estimated prevalence per serving and in the estimated 

risk of listeriosis per serving relative to the baseline. 

For each baseline, 30 simulations of 100 retail delis × 1,000,000 servings are computed with the model. 

The 2.5th and 97.5th percentiles of the mean prevalence and the 2.5th and 97.5th percentiles of the mean risk 

per serving obtained from these 30 simulations provides 95% confidence intervals for this baseline. 

Changes from the baseline are evaluated from the results of 1 simulation of 100 retail delis × 1,000,000 

servings. Results falling within the 95% confidence interval should be considered as not significantly 

different from the results obtained in the baseline. The 95% confidence intervals about the baseline are 

shown as horizontal lines on the bottom graphs. 

As can be seen in Figure 16 and Figure 17, the absence of sanitation greatly increases the estimated 

prevalence of L. monocytogenes in the sold RTE products and, therefore, the predicted public health risk 

resulting from these RTE products. Not conducting any sanitation increases the estimated risk by 41% 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

77 

(x-axis label: No Sanitation). Risk reductions can be predicted by reducing the incoming level of 

L. monocytogenes on RTE products (Reduce Level). A reduction in incoming level of L. monocytogenes

on RTE products yields a 22% decrease in the predicted per-serving risk from RTE products. 

Conceptually, preventing cross contamination by setting all the transfer coefficients to 0 (Transfers and 

Slicer to 0) also significantly reduces the predicted risk (34%). When the slicer transfer coefficients were 

not included (Transfers to 0), the predicted risk was not significantly different from the baseline, 

emphasizing the importance of the slicer in RTE product cross contamination. Any improvement in the 

design of the slicer that would reduce the transfer coefficients could have a potential beneficial impact in 

mitigating the predicted risk of listeriosis.

Figure 18 and Figure 19 display the effect that the growth scenarios have on the mean predicted risk per 

serving and the prevalence of L. monocytogenes in RTE product sold to the consumer when multiple 

niches are present. While the effects of most scenarios on prevalence are insignificant, the effects on 

relative risk are profound. Including a growth inhibitor in all RTE products (All GI) almost does away 

with any predicted risk (96% reduction). Conversely, removing growth inhibitors (No GI) increases the 

predicted risk by almost a factor of two (184%). These results emphasize the importance of mitigations 

that control the growth of L. monocytogenes in RTE product in the retail deli and in the home. The 

inclusion of growth inhibitors in RTE product that supports L. monocytogenes growth is a mitigation 

scenario that will continue to have an effect once the RTE product is sold and leaves the retail deli. While 

growth inhibitors have little impact on prevalence, they have a significant impact on L. monocytogenes

concentrations at the time of consumption, and thus on the predicted risk. Proper holding temperatures 

and reduced holding times at home also would reduce the predicted risk, but this is out of the scope of this 

study, and improvement of cold holding at home was not tested here. As previously observed in

L. monocytogenes risk assessment [3, 59], time and temperature abuse during home storage is considered 

a major contributor to the predicted risk of contracting listeriosis from RTE foods. 

While difficult to tell because of the scales, Figure 19 also shows that improved temperature control is an 

effective risk reduction mitigation. Maintaining all display case temperatures at 5°C (41°F) or less 

resulted in a 9% reduction in the predicted risk when compared with the current estimated industry 

practice.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

78 

Figure 16: Effect of various sanitation scenarios on 

the mean risk per serving and relative risk in the 

susceptible population in a retail deli with multiple 

niches

Figure 17: Effect of various sanitation scenarios on 

the prevalence and relative prevalence of

L. monocytogenes-contaminated RTE products in a 

retail deli with multiple niches
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

79 

Figure 18: Effect of various growth scenarios on the 

mean risk per serving and relative risk in the 

susceptible population in a retail deli with multiple 

niches

Figure 19: Effect of various growth scenarios on the 

prevalence and relative prevalence of

L. monocytogenes-contaminated RTE products in a 

retail deli with multiple niches

Analyses of the growth, transfers, and inactivation for a typical baseline run are shown in Figure 20 and 

Figure 21. 

Figure 20 illustrates the total number of bacteria transferred between each pair of sites as well as the total 

number of new bacteria from growth and the total number of bacteria that are discarded from 

inactivation/removal. Note that the overall number of bacteria transferred from/to RTE products (meat, 

cheese, and salad) is influenced by their relative sales for this figure: a RTE product rarely sold would 

lead to a lower total number of transferred bacteria. Significant growth is observed, particularly in deli 

meats. A significant number of bacteria end in the “washed” or “trashed” compartment (i.e., the 

compartments that count the number of bacteria that are eliminated from sanitation practices). This 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

80 

reinforces the importance of sanitation. A large number of colored cells, both in the “From” and “To” 

category, are associated with gloves. This illustrates the importance that actual handling/touching by the 

worker has on cross contamination. 

Figure 20: Total L. monocytogenes cfu grown, inactivated, and transferred between pairs of sites for a 

baseline retail deli with multiple niches (log10 scale). White areas indicate transfers that are not considered in 

the model or that are not meaningful.

Figure 21 illustrates similar matrix limited to transfers in which the bacteria transferred have been 

normalized by the actual number of contacts. Higher cfu transfers per contact are noted between deli meat 

and slicer, and to a lesser degree between deli cheese and slicer.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

81 

Figure 21: L. monocytogenes cfu transferred per actual contact between each pair of sites for a baseline retail 

deli with multiple niches (log10 scale). White areas indicate transfers that are not considered in the model or 

that are not meaningful. 

Figure 22 evaluates the duration of site contamination. The upper graph indicates that for a retail deli with 

multiple niches/transfer from the environment, the NFCS are contaminated most often (upper graph). 

When contaminated, the contamination persists the longest (lower graph). Nevertheless, this graph shows 

that the contamination remains transient on sites even in the case of regular transfer from the 

niche/environment. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

82 

Figure 22: Contamination time analysis for sites in baseline retail deli with multiple contaminated niches. 

Upper graph: fraction of time each site is contaminated during a baseline simulation. Bottom graph: mean 

number of events during which contamination persists. 

For a listeriosis case to occur through a retail deli sale, several events must occur. Contamination must be 

present in, or enter, the retail environment. Within this model, contamination can enter the retail deli by 

means of a contaminated chub or be present in the retail deli in a niche or other environmental source. 

Figure 23 illustrates the process leading from a contaminated chub to a high risk of listeriosis from 

consumption of a RTE product sold from the deli department. Thirty sales are shown from a baseline 

retail deli with transfer from the environment, although the environment contamination itself does not 

factor into this analysis. The sales and actual RTE products are shown in Figure 23a (bottom). A chub of 

cured ham with a high L. monocytogenes concentration is used for two sales: #84903 and #84909. All 

other RTE products sold are at much lower concentrations. When the RTE products are sliced, the total 

number of bacteria leaving with each sale is shown in Figure 23b. Note that these values are total cfu, not 

adjusted for weight of the sale. As expected, the two sales from the contaminated chub have very high cfu 

counts; more than 2,000 and over 6,000 cfu, respectively. The pattern after the contaminated chub is 

sliced changes. Sale #84904 is a deli cheese RTE product, so a different slicer was used, and cross 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

83 

contamination does not occur. The two subsequent sales (#84905 and #84906) are for salami and uncured 

turkey, respectively. Bacterial transfers from the contaminated slicer contaminate these sales, in a typical 

exponentially decreasing amount, until the slicer is either sanitized or all of the available bacteria are 

transferred from the slicer to sales. Subsequent sales (#84907 and #84908) thus are not contaminated. 

Therefore, this first cross contamination event contaminates two additional sales. The second 

contamination event (#84909) contaminates three additional sales. Sale #84912 is potato salad, so does 

not contact the slicer and is not involved in the cross contamination. 

The dose at consumption for each of these sales is shown in Figure 23c. For listeriosis to occur, the 

FAO/WHO dose-response model [59] indicates that very large numbers of bacteria must be consumed. 

Therefore the next process that must occur prior to a listeriosis case is significant growth during consumer 

handling (i.e., from the time of the sale to the time of consumption). This implies that the RTE product 

itself must support growth and, typically, consumer mishandling (i.e., the RTE product is in the retail deli 

for an extended period and/or at an elevated temperature). Only one of the sales in this example has both 

these features: sale #84906. On the cross contaminated uncured turkey, L. monocytogenes grew to its 

maximum concentration of 108 cfu/g and was consumed in an approximate 100 g serving. The other 

contaminated sales are either in low/non-growth RTE products or are not mishandled. 

Finally, a listeriosis case usually results when a susceptible person consumes a high dose of 

L. monocytogenes. Figure 23d shows the resulting risks of invasive listeriosis following consumption of 

one serving from these sales. For this run, the one high dose was consumed by an individual from the 

general population, so the resulting risk of illness, evaluated using the corresponding dose-response 

model, was less than 0.03%.

Given this required chain of events for each listeriosis case, it is much more likely that retail 

contamination results in very sporadic cases of listeriosis, unlike the major outbreaks in which large 

numbers of illnesses are traced back to insanitary conditions or loss of process control. Currently, cases 

known to be outbreak-associated are <1% of cases reported to the Foodborne Diseases Active 

Surveillance Network (FoodNet) [10] 

The model also illustrates the difficulty of tracing back sporadic illnesses to a specific food. Assuming 

RTE product was still available to be tested at the home and at the retail deli, testing the uncured turkey at 

the home would indicate a high concentration. However, testing at retail the exact chub the serving was 

taken from would find L. monocytogenes absent. Even without consideration of the long incubation 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

84 

period for listeriosis [122], identifying contaminated RTE food and linking this to retail deli cross 

contamination would be very difficult.

Figure 23. Timeline illustration of sales, cross contamination, and subsequent risk of listeriosis
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

85 

Baseline Condition: No niche 

For this baseline, a retail deli without any niches or highly contaminated RTE product was evaluated. The 

sanitation scenarios are shown in Figure 24 and Figure 25, and the growth scenarios are shown in Figure 

26 and Figure 27. Here, sanitation has a much lower impact, because there are fewer bacteria to remove. 

Pre-slicing increases the predicted risk by 25%. As with the niches-contaminated retail deli, reducing 

incoming L. monocytogenes levels reduces the predicted risk (24% reduction) and preventing any cross 

contamination does as well (19% reduction). Only no sanitation has a noticeable impact on the 

prevalence.

The growth scenarios are shown in Figure 26 and Figure 27. As with a niche retail deli, the impact of 

using growth inhibitors was overwhelming. Temperature control was even more effective for retail delis 

without any niches than for niche retail delis. Simply maintaining case temperatures at less than 5°C 

(41°F) resulted in a 16% reduction in the predicted risk. For a retail deli without any niches, the only way 

for external bacteria to enter the deli area is through the incoming RTE product at low concentrations. 

Preventing L. monocytogenes from growing becomes more important. In the niche retail deli, even when 

growth was completely controlled, new L. monocytogenes regularly entered because of the niches. 

A comparison of the relative effectiveness of the changes in practice in a retail deli without any niches 

and a retail deli with multiple niches is shown in Figure 28. The growth inhibitor options have been 

removed for scaling reasons. A 1:1 line (dashed) and a regression line (solid) have been added for 

reference. A linear regression is not expected. The predicted risks from a retail deli with multiple niches 

are logically higher; all the points but one fall below the 1:1 line. The degree to which they fall below this 

line is based on the arbitrary assumption of a frequency of contamination from a niche at 1 week, with a 

mean transfer of 100 cfu. Different niche loadings would have moved the points closer or farther from the 

1:1 line, as evaluated in the sensitivity analysis of Section 7.2.1. This graph illustrates which scenarios

perform significantly better or worse for the different retail deli types. 

For example, pre-slicing falls far above the regression line. Pre-slicing in a retail deli without any niches 

is a relatively worse practice than in a niche-contaminated retail deli. Conversely, the three temperature 

control options all fall far below the regression line. Temperature control is a relatively better mitigation 

for retail delis without any niches than for niche-contaminated retail delis. The lack of sanitation impacts 

a niche contaminated retail deli more than a retail deli without any niches. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

86 

Figure 24: Effect of various sanitation scenarios on 

the mean risk per serving and relative risk in the 

susceptible population in a retail deli without any 

niches

Figure 25: Effect of various sanitation scenarios on 

the prevalence and relative prevalence of

L. monocytogenes contaminated RTE products in a 

retail deli without any niches
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

87 

Figure 26: Effect of various growth scenarios on the 

mean risk per serving and relative risk in the 

susceptible population in a retail deli without any 

niches

Figure 27: Effect of various growth scenarios on the 

prevalence and relative prevalence of

L. monocytogenes contaminated RTE products in a 

retail deli without any niches
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

88 

Figure 28: Risk comparisons between niche retail deli and retail deli without any niches

Baseline Condition: highly contaminated incoming RTE product that supports growth 

For these scenarios, the incoming growth-supporting RTE product had the mean incoming

L. monocytogenes log10 concentration increased from -9.2 log10 cfu/g to -5 log10 cfu/g. This level of 

contamination is fairly high. It leads to a probability for a 2,270 g chub to be positive (≥1 cfu in the chub) 

of 32%, and a probability for this chub to have an average concentration ≥100 cfu/g of 0.83% (Table 13). 

The predicted risks include the risk linked to the incoming contaminated RTE product, (i.e., the 

contaminated RTE product was sold and consumed), and these sales were included in the risk calculation. 

A different approach to contaminated RTE product was taken during the sensitivity analysis of Section 

7.2.1, where sales from the contaminated RTE product were not recorded. 

Sanitation scenarios are shown in Figure 29 and Figure 30, while growth scenarios are shown in Figure 

31 and Figure 32. In this baseline, only pre-slicing significantly increased the predicted risk (50%). 

Removing all cross contamination had a slight beneficial effect (10% reduction). None of the other results

were notably different from those of the baseline.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

89 

Figure 29: Effect of various sanitation scenarios on 

the mean risk per serving and relative risk in the 

susceptible population for retail delis with an 

incoming contaminated RTE product that supports 

growth

Figure 30: Effect of various sanitation scenarios on 

the prevalence and relative prevalence of

L. monocytogenes contaminated RTE products for 

retail delis with an incoming contaminated RTE 

product that supports growth
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

90 

Figure 31: Effect of various growth scenarios on the 

mean risk per serving and relative risk in the 

susceptible population for retail delis with an 

incoming contaminated RTE product that supports 

growth

Figure 32: Effect of various growth scenarios on the 

prevalence and relative prevalence of

L. monocytogenes contaminated RTE products for 

retail delis with an incoming contaminated RTE 

product that supports growth

In this situation, the prevalences increase with no sanitation, even though there is little corresponding 

change in the predicted risk.

Growth mitigations showed more promise. As with all of the other retail deli types, use of growth 

inhibitors dominated the responses, and growth inhibitors for all RTE products virtually removed any 

risk. Controlling the case temperature to ≤5°C (41°F) resulted in a 12% reduction. 

The risk comparison to a niche retail deli for different scenarios is shown in Figure 33. The growth 

inhibitor scenarios were not included on the graph, to allow the risk scale to focus on the remaining 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

91 

scenarios. The 1:1 line is not included, because the scales are so different. Temperature controls are more 

effective in retail delis that have an incoming RTE food that supports growth than in retail delis with 

multiple niches. Pre-slicing is correspondingly worse in retail delis that have an incoming RTE food that 

supports growth, compared with retail delis with multiple niches. 

Figure 33: Risk comparison for niche retail deli versus retail deli with incoming RTE product that supports 

growth

It appears that the incoming levels of L. monocytogenes in the contaminated RTE product for this baseline 

are relatively high and represent a higher overall loading than in the niche retail deli. In this case, preslicing of the contaminated RTE product actually makes the situation worse. Controlling the growth in the 

relatively highly contaminated RTE product is effective, because the model does not include growth at 

FCS or NFCS. Therefore, while a niche contaminates an associated site, growth does not occur at the site. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

92 

Baseline Condition: highly contaminated incoming RTE product that does not support growth

As with the previous scenario, a retail deli with a highly contaminated incoming RTE product is modeled. 

In this case, the product does not support growth. As before, the mean of the log10 concentration of

L. monocytogenes in this incoming RTE product is greater than the one from other products (from -9.2 

log10 cfu/g to -5 log10 cfu/g). 

Sanitation scenarios are presented in Figure 34 and Figure 35; growth scenarios are shown in Figure 36

and Figure 37. The theoretical “no cross contamination” scenario (Transfers and Slicer to 0) in the deli is 

quite effective (61 % reduction). If cross contamination is completely prevented, the high bacteria 

concentrations in the contaminated RTE product cannot spread to other RTE products, notably to RTE 

products that support growth. For this situation, pre-slicing also reduces the predicted risk (34% 

reduction). As expected, the theoretical “no sanitation” scenario increases the predicted risk (24% 

increase). In our setting, the “Separate Slicer” scenario implies that deli meat products that support 

growth are sliced on a specific slicer, and all other products are sliced on the other slicer. In this baseline, 

this scenario leads to a higher number of potentially cross contaminated RTE products. As a consequence, 

separate slicers for deli meat that support growth (with or without separate cases) increase the predicted 

risk by 23% in this baseline. More specific settings could be tested in future runs of this model. 

Growth inhibitors are still critically important. For an incoming contaminated RTE product that does not 

support growth, temperature control is significantly different from the baseline, although slightly. 

Controlling the case temperature to ≤5°C (41°F) results in an 8% reduction. Because the contaminated 

RTE product does not support growth at any temperature, temperature control affects only the other RTE 

products. 

The risk comparisons between retail delis with multiple niches and retail delis with incoming 

contaminated non-growth RTE product are shown in Figure 38. The points are above the 1:1 reference 

line, indicating higher predicted risk for the incoming contaminated chubs baseline, compared with the 

baseline with multiple niches. “No cross contamination,” “pre-slicing,” and “no sanitation” scenarios lead 

to lower risks, relative to the niche retail deli. Separate slicers lead to relatively higher risks. 

A comparison between the conditions with increased contamination in a growth versus non-growth RTE 

product is shown in Figure 39. Both incoming contaminated RTE products have the same mean

L. monocytogenes levels (mean of the log10 concentration increased from -9.2 to -5 log10 cfu/g). The 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

93 

non-growth RTE product actually has a higher sales level, but the predicted risks from the 

growth-permitting RTE product are always substantially higher. The 1:1 line cannot be shown, because of 

the difference in scale. Having “no cross contamination” while instituting effective temperature controls 

appears to reduce risk more for RTE product that supports growth than for RTE products that do not 

support growth. Growth, especially when the RTE product is heavily contaminated, is a major source of 

new bacteria within the retail environment. Temperature control to reduce or prevent growth reduces the 

predicted risk when the contaminated RTE product supports growth. Similarly preventing cross 

contamination from the growth chubs, particularly as growth increases the concentrations further, also 

reduces the risk well. On the other hand, pre-slicing RTE foods increases the predicted risk significantly 

more when foods support the growth of L. monocytogenes. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

94 

Figure 34: Effect of various sanitation scenarios on 

the mean risk per serving and relative risk in the 

susceptible population for retail delis with an 

incoming contaminated RTE product that does not 

support growth

Figure 35: Effect of various sanitation scenarios on 

the prevalence and relative prevalence of

L. monocytogenes contaminated RTE products for 

retail delis with an incoming contaminated RTE 

product that does not support growth
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

95 

Figure 36: Effect of various growth scenarios on the 

mean risk per serving and relative risk in the 

susceptible population for retail delis with an 

incoming contaminated RTE product that does not 

support growth

Figure 37: Effect of various growth scenarios on the 

prevalence and relative prevalence of

L. monocytogenes contaminated RTE products for 

retail delis with an incoming contaminated RTE 

product that does not support growth
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

96 

Figure 38: Risk comparison for niche retail deli versus a retail deli with incoming product that does not 

support growth

Figure 39: Risk comparison for niche retail deli versus retail deli with incoming RTE product that does not 

support growth versus one that does support growth
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

97 

Baseline Condition: No niche, with required temperature control 

The scenarios for a retail deli without any niches that maintains its deli case at ≤5°C (41°F) are shown in 

Figure 40 through Figure 43. As expected, the absolute magnitudes of the predicted risks are lower than 

in a baseline condition considering a retail deli without any niches. “No sanitation” and “pre-slicing” 

increase the predicted risk by 12% and 19%, respectively. Preventing all retail cross contamination and 

reducing the incoming L. monocytogenes level reduce the estimated risk by 19% and 22%, respectively. 

Growth inhibitors are still extremely effective.

Figure 40. Effect of various sanitation scenarios on the 

mean risk per serving and relative risk in the 

susceptible population for retail deli with temperature 

control.

Figure 41. Effect of various sanitation scenarios on 

the prevalence and relative prevalence of

L. monocytogenes-contaminated RTE products for 

retail deli with temperature control.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

98 

Figure 42. Effect of various growth scenarios on the 

mean risk per serving and relative risk in the 

susceptible population for retail deli with temperature 

control.

Figure 43. Effect of various growth scenarios on the 

prevalence and relative prevalence of

L. monocytogenes-contaminated RTE products for 

retail deli with temperature control.

Baseline retail deli with multiple niches and temperature control 

Results for retail delis with multiple niches, but with temperature control of the deli case at ≤5°C (41°F), 

are provided in Figure 44 to Figure 47. Lack of any sanitation increases the predicted risk by 50%; 

eliminating cross contamination and reducing incoming L. monocytogenes levels reduces the predicted

risk by 30% and 16%, respectively. Growth inhibitors remain extremely effective.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

99 

Figure 44. Effect of various sanitation scenarios on the 

mean risk per serving and relative risk in the 

susceptible population for retail deli with multiple 

niches and with temperature control

Figure 45. Effect of various sanitation scenarios on 

the prevalence and relative prevalence of

L. monocytogenes contaminated RTE products for 

retail deli with multiple niches and with 

temperature control
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

100 

Figure 46. Effect of various growth scenarios on the 

mean risk per serving and relative risk in the 

susceptible population for retail deli with temperature 

control

Figure 47. Effect of various growth scenarios on the 

prevalence and relative prevalence of

L. monocytogenes contaminated RTE products for 

retail deli with temperature control

7.3. Responses to risk management questions 

The relative risk associated with an alternative scenario, as compared with the risk calculated for a 

baseline condition, was evaluated within each baseline for the susceptible population. The scenarios were 

developed according to risk management questions provided in Section 3. Figure 48 through Figure 60

illustrates the results for the various alternative practices across baselines. Remember, the absolute values 

for the predicted risk change drastically for each baseline. With the exception of the growth inhibitor 

analysis, all the relative risks in the graphs are scaled the same, to make comparison between the 

predicted risks associated with different risk management scenarios more apparent. The baseline 

conditions and the scenarios are described in 7.1.1 and 7.2.2, respectively. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

101 

7.3.1 What would be the impact, on the prevalence of L. monocytogenes in RTE products 

sold in retail delis and on the corresponding mean risk of invasive listeriosis per 

serving, of practicing more frequent or more extensive cleaning procedures for foodcontact surfaces and/or non-food-contact surfaces than is currently specified in the 

2009 FDA Food Code 2009? 

Figure 48: Relative risk comparison for sanitation options

Wash & Sanitize: increase the effectiveness of cleaning from simply washing to washing and sanitizing [i.e., from 

an average log10 reduction issued from a Pert(-1.5, -0.5, 0) to a Pert(-8, -6, -1.5)]; Clean 8 Sporadic: increase the 

number of sites sporadically cleaned from 4 to 8; No Sanitation: do not conduct any wiping, washing, or sanitizing; 

No Sporadic Cleaning: only clean FCS to the minimum required by the 2009 FDA Food Code, but do not conduct 

the additional sporadic cleanings (as was observed by Lubran et al. [28]).
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

102 

The relative risks for changes in sanitation are shown in Figure 48. Figure 48c clearly indicates that 

failing to clean and sanitize all surfaces results in a significant increase in the predicted risk of listeriosis. 

Nevertheless, it seems that modifying any of the single sanitation-related practices individually [i.e., 

cleaning more effectively (Figure 48a), increasing the number of sporadic cleaning sites (Figure 48b), or

not conducting any sporadic cleaning (Figure 48d)] had little impact on the relative risk in each retail deli 

condition studied. Specific scenarios for the cleaning frequency and disinfection of the slicer could be 

developed in future versions of this model.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

103 

7.3.2 What is the impact of increasing the use of single-service gloves in the retail 

environment?

Figure 49: Relative risk comparison for glove use

No Glove: do not use gloves when serving customers; Gloves Every Serving: change gloves for every serving.

The lack of glove use consistently increases the predicted risk across all retail deli types (Figure 49a), 

often at statistically significant levels. Thus, glove use is recommended to aid in L. monocytogenes
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

104 

control and would be expected to be critical for other foodborne pathogens, such as norovirus or Shigella

[123]. Changing gloves for every serving did not result in a significant change (Figure 49b), but recall 

that gloves already are changed for approximately 65% of servings in baselines. 

7.3.3 What if scale touch pads, refrigerator and deli case handles and other frequently 

touched non-food-contact surfaces were considered food-contact surfaces and were 

therefore required to be cleaned and sanitized at a minimum frequency?

These items were required to be cleaned and sanitized every four hours and, as a result, could then be 

touched by gloved hands without requiring a decontamination action afterwards. 

Figure 50: Relative risk comparison for treating NFCS as FCS

NFCS as FCS: treat NFCS as if they were FCS that must be cleaned every 4 hours, according to the FDA Food 

Code. 

Treating NFCS as FCS for cleaning purposes had little impact on the predicted risk. The only retail deli 

type where the risk reduction was statistically significant was for a retail deli without any niches that 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

105 

implemented temperature control. In this case, in which L. monocytogenes levels are low and growth is 

limited, the additional cleaning might be beneficial, relative to the baseline low risk. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

106 

7.3.4 What if practices were in place so that no cross contamination occurred in delis?

No additional L. monocytogenes added to incoming RTE product. 

Figure 51: Relative risk comparison for transfer coefficients
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

107 

Transfers to 0: set cross contamination transfer coefficients to 0 (i.e., no cross contamination occurs). This setting 

does not affect the slicer, (i.e., cross contamination can still occur from one sale to the next through the slicer). See 

the next scenario; Transfers and Slicer to 0: set cross contamination transfer coefficients to 0 (i.e., no cross 

contamination occurs, for all sites, including the slicer). 

Setting the transfer coefficients to 0 prevents cross contamination for all sites except the slicer. This 

approach had no significant impact on the relative risk (Figure 51a). Including the slicer in the sites, 

however, greatly reduced the predicted risk (Figure 51b) across all retail deli types evaluated. This 

highlights the importance of the slicer in cross contamination. The importance of the slicer in potential 

cross contamination has been demonstrated experimentally [see for example 23, 37, 54, 58, 124]. Our 

results confirm that this element may be of major importance when all cross contamination events are 

considered in a deli department setting.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

108 

7.3.5 What if display cases were not touched with gloved or bare hands?

Use tissues or automatic door to open/shut display case to reduce cross contamination. 

Figure 52: Relative risk comparison for contact between gloves and case handle

No-Contact Glove Case: glove/hands are not used to open the deli case (e.g., if a floor switch is used.) 

Preventing contact between the case handle and hand/gloves had no significant impact in any of the 

baselines studied. Actually, this scenario is a subset of the scenario Transfers to 0 [cross contamination 

transfer coefficients to 0 (i.e., no cross contamination occurs)], with one transfer set to 0. This result 

confirms that, within a baseline, no single cross contamination event has a major impact on the predicted 

risk, with the exception of the cross contamination within the slicer. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

109 

7.3.6 What would be the impact if the level/frequency of L. monocytogenes contamination 

were reduced in RTE foods coming into the retail deli?

Figure 53: Relative risk comparison for reducing incoming level

Reduce Level: lower the mean incoming L. monocytogenes concentration on all RTE products from a mean of the 

log10 of -9.2 to a mean of the log10 of -9.5. This leads to an average prevalence for a 2,270 g chub of 2.35% vs. 

2.97% in the baseline (Section 6.5.2).

Reducing the incoming L. monocytogenes concentration significantly reduced the predicted risk. 

Incoming L. monocytogenes represents one of the major routes through which the bacteria can come in 

contact with FCS and cross contaminate in this model. Even in situations in which L. monocytogenes

from the environment are regularly introduced into the deli department, the level of bacteria in incoming 

RTE product does have an impact on the final relative risk of listeriosis from the consumption of RTE 

products from the deli department. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

110 

7.3.7 What would be the impact of “pre-slicing” all RTE products vs. “slicing to order”? 

Following the hypothesis that less cross contamination occurs in the morning prior to other cross 

contamination events: 

Figure 54: Relative risk comparison for pre-slicing

Pre-slice: pre-slice all chubs of RTE product in the morning after cleaning. For each RTE product, a quantity of 

food equal to the median of the daily sales is pre-sliced every morning. When a consumer orders a RTE product, the 

food worker serves the pre-sliced RTE product, until all the pre-sliced quantity is sold. If needed, additional RTE 

product is sliced to order. At the end of the day, the remaining pre-sliced RTE product is discarded.

Pre-slicing generally increased the predicted risk, often substantially and especially if a contaminated 

growth-supporting chub was present. Based on a deeper study of the model outputs (analysis per serving 

on a short run, rather than overall statistics - results not shown), it was determined that this is due to a 

relatively important contamination of the slicer during the pre-slicing process, following one single 

contaminated chub. A high number of RTE product servings are then cross contaminated, leading to a 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

111 

higher predicted risk. A retail deli with an incoming contaminated non-growth chub was the only baseline 

situation in which pre-slicing led to a significantly lower predicted risk. In this situation, pre-slicing leads 

to a distribution of the bacteria to the same category of (non-growth) RTE products, rather than to various 

(growth and non-growth) RTE product if sliced throughout the day. This limits the contamination of the 

RTE product that supports the growth, and thus the predicted risk.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

112 

7.3.8 What would be the potential public health impact of using separate slicers and/or 

separate counters for RTE products that permit growth of L. monocytogenes and for 

RTE products that do not?

Figure 55: Relative risk comparison for separate slicers and cases

Separate Slicer: use a separate slicer for RTE products that support growth versus those that do not; Separate 

Slicer Case: use a separate slicer and a separate case for RTE products that support growth versus those that do not.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

113 

Figure 55 shows that there was minimal impact on the predicted risk relative to the baseline associated 

with establishing one slicer for RTE product that supports growth of L. monocytogenes and another for 

RTE product that does not support growth of L. monocytogenes. The exception was the condition in 

which a contaminated chub did not support the growth of L. monocytogenes. Similar impacts on the 

calculated relative risk were observed when separate slicers were used and the different RTE products 

were put into separate deli cases. This may be related to the fact that in the baseline matrix of contacts 

(Section 6.5.1), it was assumed that in larger retail deli with two slicers, one slicer is used only for deli 

cheese and that the other slicer is used for deli meat and deli cheese. In the separate slicer scenario, since 

one slicer would be used exclusively for high-growth product, the slicer for the contaminated cheese 

would be used for more products, and, as a consequence, the contaminated cheese would incidentally 

contaminate more products than in the baseline, including some additional “low-growth” product. The 

resulting risk would be higher. Other matrices of contact could be tested in the future. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

114 

7.3.9 What if food workers do not slice RTE products directly onto their gloved hands? 

Figure 56: Relative risk comparison for not slicing onto gloves

Do Not Slice Onto Gloves: the food worker collects the sliced RTE product directly onto a deli tissue rather than 

slicing it directly onto a gloved hand, which represents the baseline condition.

Figure 56 suggests that slicing RTE product onto deli paper, rather than onto gloves, had little impact on 

the relative risk predicted by the model. Slicing onto gloves was a behavior observed during the

observational study [28]. The model suggests that eliminating this practice would not play a major role, 

compared with some of the other mitigations directed at preventing cross contamination.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

115 

7.3.10 What is the impact of L. monocytogenes growth in retail delis?

Figure 57: Relative risk comparison for fixed temperature control

Baseline: All RTE product in cases maintained at the actual temperatures observed in Ecosure dataset [19];

Temp = 5°C: Retail deli; all RTE products in cases consistently maintained at a temperature of 5°C (41°F); No 

Growth (T = -5°C): Retail deli; all RTE products in case consistently maintained at a temperature of -5°C (23°F) 

(no potential L. monocytogenes growth).
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

116 

Improving temperature controls in display cases to limit growth resulted in a lower predicted relative risk 

across all retail deli types. Baseline retail delis that included temperature control as part of the baseline are 

not shown. Note that the Temp = 5oC (41°F) alternative represents an increase in the temperature for 

some of the RTE products, compared with the baseline. By reducing temperature of display cases to 5oC, 

the overall predicted risk was still reduced. The “no growth” scenario (i.e., deli case temperature set 

to -5oC) results in a 20% reduction in predicted risk. This further demonstrates the importance of 

controlling the growth of L. monocytogenes in RTE products, as suggested in previous risk assessments 

[3, 59]. 

7.3.11 What would be the potential public health impact of complete compliance to the 

cold holding requirements for certain RTE foods in deli cases [hold at 41°F (5°C) or 

less]? 

Figure 58: Relative risk comparison for temperature control

Temp <= 5°: Use the case temperature distribution as observed [19], but remove any temperatures greater than 5°C

(41°F). This implies that all retail delis exceeding the FDA Food Code recommendation come into compliance.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

117 

Unlike the previous analysis, in which all retail deli cases were set to a fixed temperature, this analysis 

uses the existing temperature distribution, but removes those that exceed 5oC. The model was used to 

study the effect that full compliance with the 2009 FDA Food Code [27] temperature requirements would 

have on the predicted risk, as compared with what Ecosure data suggest is typical practice. An 

approximate 10-15% reduction in predicted risk was achieved. The effect was greatest in a retail deli 

without any niches. The model predictions suggest that a reduction of the risk of listeriosis could be 

achieved by better compliance with recommended RTE product storage temperatures in the deli 

environment. FDA’s 2008 Retail Risk Factor Study revealed that, in delis, the failure to control RTE 

product holding temperatures and times was the risk factor with the highest “Out of Compliance” 

percentage. For example, in 60% of the 98 retail delis studied by FDA in 2008, at least one observation 

was made in which food requiring temperature control was not held at 41oF or below, as specified in the 

FDA Food Code [18]. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

118 

7.3.12 What would be the potential public health impact of shortening the time a RTE 

product can be used in retail deli departments?

Figure 59: Relative risk comparison for shortening the time a RTE product can be used in a retail deli 

department

Shorten the time a RTE product can be used: shorten the length of time RTE product can be held in the retail deli 

before being sold or disposed, from 7 to 4 days. 

Figure 59 suggests that shortening, from 7 days to 4 days, the maximum time a refrigerated RTE product 

that supports growth of L. monocytogenes is allowed to remain on hand in the retail deli after opening or 

preparation has little effect on the predicted risk. Under the current model, the time from when the chub is 

opened until it is completely sold is generally shorter than the 7-day FDA Food Code requirement. Note 

that the model does not currently simulate refrigerated storage prior to the chub/deli salad bulk being 

opened. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

119 

7.3.13 What would be the potential public health impact if all or none of the RTE products 

(e.g., deli meat, deli salads, and cheese) coming into the retail deli were formulated 

with growth inhibitors?

Figure 60: Relative risk comparison for growth inhibitor use

All GI: all RTE products sold that support growth are considered to have growth inhibitors; No GI: none of the 

RTE products that support growth are considered to have growth inhibitors. 

Of all the scenarios tested, increasing or decreasing the percentage of RTE meat and poultry products and 

deli salads that contain L. monocytogenes growth inhibitors consistently had the greatest impact on the 

relative risk of listeriosis predicted by the model. Note that the risk scale (y axis) is different for Figure 60
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

120 

than for the other figures. Using growth inhibitors in deli meats and deli salads almost completely 

mitigated the risk of listeriosis – by almost 100% reduction for all risk assessment model baselines 

considered in this analysis. Growth inhibitors continue to prevent growth even after the RTE product 

leaves the retail deli. If all RTE meat and poultry products and deli salads contain no growth inhibitors, 

then the risk assessment model predicts an almost doubled risk of listeriosis from these products over 

most baseline conditions. The percentage increase in relative risk was more modest for the baseline 

condition that considers a highly contaminated growth-supporting deli meat chub. Actually, this more 

modest percentage only reflects the greatest absolute risk of listeriosis for this baseline condition. 

These results confirm the overwhelming importance of the growth of L. monocytogenes during retail and 

home storage, compared with other parameters in L. monocytogenes risk assessments, as has been 

consistently observed [3, 59]. 

7.4. Verification

Given the over-parameterization in the risk assessment model, a formal calibration (e.g., minimizing 

some objective function) or a validation is currently not possible. Nevertheless, some checks and controls 

were done with regard to the available literature and through studies specifically developed to inform the 

current risk assessment model.

7.4.1 Mass balance

Mass Balance, Transfer Matrix 

Figure 61 depicts the sources of incoming and outgoing L. monocytogenes for the retail deli system.

L. monocytogenes enter the retail deli through i) contaminated incoming raw RTE products; ii) niches (as 

currently written, the risk assessment model does differentiate the source of L. monocytogenes from a 

niche in the retail deli versus those resulting from contaminated incoming RTE product); or iii) the 

growth of L. monocytogenes on RTE product. L. monocytogenes are either on outgoing RTE product sold 

to consumers, eliminated through wiping, washing and disinfection of retail deli surfaces, or disposed of 

in the trash (e.g., on used gloves or expired RTE product). 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

121 

Deli Department

Niche Growth

Incoming 

Raw 

Product

(Chubs,

Salad Bulks)

Wiped, 

Washed,

Disinfected

Trashed

(Gloves, End of Chub)

Sold

Figure 61. Incoming and outgoing bacteria in the L. monocytogenes in retail model

At any time, the sum of the L. monocytogenes entered in the system was equal to the sum of

L. monocytogenes currently in the system plus the sum of L. monocytogenes that went out of the system. 

The checking of this mass balance additionally afforded a cross-check of proper functioning of this risk 

assessment model. The mass balance was controlled in all scenarios described in this report.

7.4.2 Surveys of L. monocytogenes in RTE foods

While validation is preferable, survey data were used to help establish parameters within the model. Two 

large datasets are available that describe the distribution of L. monocytogenes concentrations in RTE 

products. The first is a study by the National Food Processors Association [14] and the second is derived 

from a National Alliance for Food Safety and Security (NAFSS) study [16] [6]. Figure 62 illustrates the 

upper predicted tail of the cumulative distribution function for the incoming deli products and the deli 

products leaving the retail deli with niches and those without any niches (baselines). Superimposed are 

the NAFSS points for deli meat, the only food groups studied. It appears that the different retail deli 

baselines capture this critical portion of the distribution.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

122 

Figure 62: Comparison of predicted model distributions with observed retail deli observations

While this comparison should not be considered as a complete validation, because various pathways 

could lead to these results, this graph indicates that the model results are not inconsistent with observed 

data.

7.4.3 Virginia Tech mock deli

A mock retail deli study [29] was conducted, in which known sites were contaminated using an abiotic 

surrogate (GloGermTM); the mock retail deli was operated for a fixed period; and the resulting 

contaminated location recorded. Photographs under UV light were evaluated by a trained sensory panel, 

to estimate levels of surrogate contamination. As a manipulative study, the research directly links 

contaminated sites to resulting contaminated locations. Early results from this research were used to 

identify missing transfer events within the cross contamination model. Results are shown in Figure 63, 

with the color intensity and size of the circle indicating the amount of GloGermTM transfer from an 

initially contaminated site to another site in the deli. In general, initial glove and initial slicer blade 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

123 

contamination spread the surrogate across the most sites. This study serves as a validation of the 

conceptual model shown in Figure 4 and mass transfers illustrated in Figure 63. 

Figure 63: Mock retail deli results [29]. Size and color intensity indicate amount of surrogate transferred 

from source to recipient location. 

7.4.4 Cornell University expert elicitation

Hoelzer et al. [31] published an expert elicitation study on L. monocytogenes transfer within retail delis. 

Table 17 presents the major conclusions and compares these to the conceptual cross contamination model. 

The first four columns are adapted from the authors’ paper. The “Median Result” represents the 

percentage of experts who believe that the given transfer can occur. The “Percent Very Confident 

Experts” indicates a self-reported degree of confidence in their answers. The final column added for this 

report represents whether a given transfer can occur within the model.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

124 

Table 17: Comparison of expert elicitation to cross contamination model structure

L. monocytogenes transfer Median Result 

(%)

(Source [31])

Percent “very 

confident” experts

(Source [31])

Included in Model?

Source Recipient

Slicer blade RTE 

producta

86 89 Yes

Slicer blade RTE 

productb

48 22 Yes

Slicer blade Hands 23 39 Yes

Slicer blade guard Hands 22 35 Yes, but slicer treated as one location.

Cutting board RTE 

product

75 56 Yes, if cutting board treated as FCS.

Cutting board Hands 47 39 No

Scale touchpad Hands 55 59 Yes, but scale treated as one location.

Scale weigh table RTE 

product

15 43 Yes, but scale treated as one location.

Deli case handle Hands 53 90 Yes

Deli case RTE 

product

86 58 No

Deli preparation sinkc RTE 

product

48 41 Yes

Deli preparation sinkd RTE 

product

5 47 No

Walk-in cooler door handle Hands 63 47 No, walk in cooler not included as a 

site.

Walk-in cooler floor FCS 4 47 No, walk in cooler not included as a 

site.

Knife rack FCS 41 22 Yes, indirectly (utensils, e.g., knives, 

can contact RTE product and hands).

Central floor drain FCS 5 15 No a

Transfer to first slice sliced on contaminated slicer; bTransfer to 10th slice sliced on contaminated slicer; cchub set down in sink 

during unwrapping; d

chub not set down in sink during unwrapping.

The model thus includes all the perceived major routes of transfer. The major exceptions are the lack of a 

walk-in cooler site within the model and the lack of transfer from case to RTE product. The risk 

assessment model includes and assumes that the RTE product chubs are always wrapped when returned to 

the retail deli, thus limiting contact. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

125 

8. Summary of Risk Assessment Results

This QRA provides information on the predicted risk of listeriosis associated with the consumption of 

RTE foods prepared and sold in the deli of a retail food store and examines how the predicted risk may be 

impacted by different practices in a retail deli (e.g., sanitation, temperature control, and worker behavior). 

8.1. Predictions of Absolute Risk

The predicted risk of listeriosis per serving of RTE food (hereafter referred to as “absolute risk”37) was 

evaluated for two U.S. populations: 1) the “susceptible population” (e.g., older adults, fetuses, newborns, 

and those who have immune-compromising conditions, as defined by FAO/WHO [59]); and 2) the 

remaining U.S. population (i.e., referred to as the “general population” in this QRA). Table 18 shows the 

predicted absolute risk to the two populations and six different baseline conditions that may characterize a 

retail deli and the RTE food it serves at different times over the course of operations. The baseline 

conditions are:

1) a retail deli with multiple niches or environmental transfers that regularly release 

L. monocytogenes to food-contact surfaces; 

2) a retail deli with no niches or environmental L. monocytogenes transfer; 

3) a retail deli with no niche or environmental L. monocytogenes transfer, with one incoming 

RTE product contaminated at levels higher than other products in the deli (mean of the log10: 

-5 log10 cfu/g vs. -9.2 log10 cfu/g) and with the incoming contaminated RTE product 

supporting growth; 

4) a retail deli with no niche or environmental L. monocytogenes transfer, with one incoming 

RTE product contaminated at levels higher than other products in the deli (mean of the log10: 

-5 log10 cfu/g vs. -9.2 log10 cfu/g) and with the incoming contaminated RTE product not 

supporting growth; 

5) a retail deli with multiple niches and compliant with the 2009 FDA Food Code guidance for 

temperature control (≤41°F); and, 

6) a retail deli without any niches and with compliant temperature control (≤41°F). 

 

37 When interpreting the results, it is important to keep in mind that the specific values used in the QRA to 

characterize the baseline conditions are merely representative of a range of values that could possibly occur. For 

example, not all retail deli niches will transfer a mean of 100 cfu on a weekly frequency, as modeled in the 

“Multiple Niche 100W” baseline. Also, not all incoming contaminated product will have a mean log10 contamination 

of -5 log10 cfu/g, as the incoming product baselines are modeled. The range of values used in the models for various 

scenarios are evaluated through the sensitivity analysis discussed in Section 7.2.1. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

126 

In general, across all six baseline conditions, the predicted absolute risk for the susceptible population is 

much higher, compared with the predicted risk for the general population (Table 18). This result is

expected, because of the differences in the dose-response relationships for these two populations (see 

Section 6.4.4). For any given dose of ingested L. monocytogenes, individuals from the susceptible 

population are predicted to have a higher probability of illness, compared with the general population. 

The predicted absolute risk to consumers in the general population ranges from 1.5 × 10-9 to 37.3 × 10-9

per serving and for susceptible consumers ranges from 1.2 × 10-7 to 16.6 × 10-7 (a ~45-fold higher risk 

under all baseline conditions examined in this QRA). 

Table 18: Predicted absolute risk of invasive listeriosis per serving of ready-to-eat food sliced or prepared and 

sold at retail delis

U.S. Populations 

Evaluated1

Baseline Retail Deli Conditions2

Multiple 

Niche 100W No Niche

Incoming 

Growth 

Chub

Incoming 

Non-growt

h Chub

Temp. 

Control

Niche & 

Temp. 

Control

Susceptible population 1.7×10-7 1.4×10-7 16.6×10-7* 2.8×10-7** 1.2×10-7 1.5×10-7

General population 3.8×10-9 3.1×10-9 37.3×10-9 6.3×10-9 2.7×10-9 3.3×10-9

Note: Detailed discussion of QRA model mathematics, assumptions, and data are provided earlier in this technical report.

1

The U.S. population was divided in two subpopulations for the purpose of this risk assessment: the susceptible population (e.g., 

older adults, fetuses, newborns, and those who have immune-compromising conditions, according to FAO/WHO [59] definition) 

and the remaining population (referred to as the “general population”).

2

Description of the baseline conditions: Multiple Niche 100W = a retail deli with multiple niches that releases L. monocytogenes

to food-contact surfaces at a rate of 100 cfu on an average weekly frequency; No Niche = a retail deli with no niches or 

environmental L. monocytogenes transfer; Incoming Growth Chub = A retail deli with no niche or environmental 

L. monocytogenes transfer, with one incoming RTE product that is contaminated at levels higher than those of other products in 

the deli (mean of the log10: -5 log10 cfu/g vs -9.2 log10 cfu/g) and that does support growth of L. monocytogenes; Incoming 

Non-growth Chub = A retail deli with no niche or environmental L. monocytogenes transfer, with one incoming RTE product 

that is contaminated at levels higher than those of other products in the deli (mean of the log10: -5 log10 cfu/g vs -9.2 log10 cfu/g)

and does not support the growth of L. monocytogenes; Niche & Temperature Control = a retail deli with multiple niches and 

compliant with the 2009 FDA Food Code guidance for temperature control (≤41°F): Temperature Control = a retail deli 

without any niches and with compliant temperature control.

* The corresponding risk was 2.9 × 10-7 when the servings directly from the incoming highly contaminated product were 

removed from the calculation of the risk (see Section 7.2.1). 

** The corresponding risk was 2.3 × 10-7 when the servings directly from the incoming highly contaminated product were

removed from the calculation of the risk (see Section 7.2.1). 

8.2. Evaluation of the Impact of Differences in Baseline Conditions 

Comparisons among the six baselines provide insight about the extent to which some retail conditions 

impact the predicted risk of listeriosis. Two example comparisons follow, to illustrate the impact of retail 

conditions on the predicted absolute risk estimates shown in Table 18. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

127 

Temperature Control: 

A comparison of retail delis that do not have niches or environmental transfer of L. monocytogenes (“No 

Niche” column) with those that also ensure storage temperatures maintained at ≤41°F (“Temp. Control” 

column) results in a reduction in the predicted absolute risk (from 1.4 × 10-7 to 1.2 × 10-7 for the 

susceptible population). A similar reduction (i.e., from 1.7 × 10-7 to 1.5 × 10-7 for the susceptible 

population) was predicted for retail delis with niches (“Multiple Niche 100W” column), when compared 

with those with niches that also maintain strict temperature control (“Niche & Temp. Control” column).

The importance of the temperature control within a baseline condition is further illustrated in the Scenario 

Analysis section below.

L. monocytogenes on Incoming RTE Products: 

A comparison of retail delis that do not have niches or environmental transfer of L. monocytogenes (“No 

Niche” column) with similar retail delis that also have more highly contaminated incoming RTE products 

(whether or not they support growth) provides information on the increased predicted risk from (a) the 

highly contaminated incoming product and; (b) those products subsequently cross contaminated in the 

deli. 

When the incoming highly contaminated RTE product is one that does not support growth of 

L. monocytogenes, the predicted absolute risk increases from 1.4 ×10-7 to 2.8 × 10-7 for the susceptible 

population (compare “No Niche” with “Incoming Non-growth Chub”; Table 18). When the highly 

contaminated incoming RTE product is one that supports growth of L. monocytogenes, the predicted 

absolute risk increases to 16.6 × 10-7 for the susceptible population (compare “No Niche” with “Incoming 

Growth Chub”). The predicted absolute risk of product from stores that have a highly contaminated 

incoming RTE product that supports growth of L. monocytogenes is six times higher than the risk from 

stores that have a highly contaminated incoming RTE product that does not support growth of 

L. monocytogenes (16.6 × 10-7 vs. 2.8 × 10-7). 

However, when the servings directly associated with the incoming highly contaminated RTE product are 

removed from the calculation of the risk, the increase in the predicted absolute risk is only the risk 

associated with retail cross contamination. When the highly contaminated incoming RTE product is one 

that does not support growth of L. monocytogenes, the predicted absolute risk increases from 1.4 ×10-7

(“No niche”; Table 18) to 2.3 × 10-7 (Table 18 footnote and Section 7.2.1). This is almost the same 

increase in predicted absolute risk as when all RTE servings are included in the risk calculation (i.e., 

2.8 × 10-7). Most of the increase in the predicted absolute risk of products from these stores results from 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

128 

cross contamination. This result, in addition to the ones evaluating cross contamination (see Scenario 

Analysis section below), illustrates the importance of retail cross contamination for RTE products that do 

not support growth of L. monocytogenes. 

When the servings directly associated with the incoming highly contaminated product are removed from 

the calculation of the risk for the highly contaminated incoming RTE product that supports growth of 

L. monocytogenes, the predicted absolute risk increases from 1.4 ×10-7 (“No niche”; Table 18) to 

2.9 × 10-7 for the susceptible population (Table 18 footnote and Section 7.2.1). The slightly higher 

predicted absolute risk for highly contaminated incoming RTE products that support growth (2.9 × 10-7

vs. 2.3 × 10-7) is due to growth of L. monocytogenes on the products while in the retail delis, allowing for 

additional L. monocytogenes to cross contaminate other RTE foods. Most notable, however, is that the 

majority of the predicted absolute risk results directly from product contaminated during processing and 

growth of L. monocytogenes on these products during retail and home storage (i.e., 16.6 × 10-7 vs. 

2.9 × 10-7 when only cross contaminated servings are considered). This result, in addition to the ones 

evaluating the impact of growth inhibitors (see “Scenario analysis” section below), illustrates the 

overwhelming importance of the growth of L. monocytogenes during retail and home storage for RTE 

products that support its growth. 

Overall, the baseline conditions indicate that 1) retail delis without niches and retail delis that control 

temperature lead to lower predicted risk of listeriosis, and 2) retail delis with incoming RTE products that 

are highly contaminated with L. monocytogenes, notably if the product supports growth, and retail delis 

with niches lead to higher predicted risk of listeriosis.

8.3. Scenario Analysis

For each of the six baseline retail conditions (listed above), this QRA was used to evaluate the public 

health impact of 22 different “what if” scenarios (i.e., changes in sanitary practices, worker behaviors, 

product formulation, cross contamination, and product storage temperature and duration). In total, this 

QRA provides 126 summary public health findings related to retail practices (Table 19). 

In Table 19, each column represents one of six different baseline conditions that may be present in retail 

delis. Each row shows the percentage change in the risk per serving for the susceptible population relative 

to the baseline condition for each “what if” scenario (hereafter referred to as “relative risk”). The 

scenarios are organized according to what they evaluate; i.e., changes in: 1) sanitation, 2) worker 

behavior, 3) use of growth inhibitors, 4) cross contamination, and 5) storage temperature control. In this 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

129 

table, positive values represent an increase, while negative values represent a decrease in the predicted 

relative risk per serving. [Note: Table 19 provides a summary of the results for each scenario, relative to 

the baseline for only the susceptible population. While the predicted absolute risks were different between 

the susceptible populations and general population, the predicted relative change in risk is similar for both 

populations]. 

The relative effectiveness of a scenario applied to a specific baseline condition can be assessed by reading

down the columns in Table 19. The effectiveness of a change in practice across different operating 

conditions can be assessed by reading across each row in Table 19, keeping in mind the magnitude of the 

predicted risk for that scenario (shown in Table 18). Some scenarios predict that the mitigation would not 

be very effective in reducing the predicted risk on a per-serving basis (e.g., no contact between the glove 

and the case), while others (e.g., pre-slicing) can either be slightly beneficial or highly detrimental, 

depending on retail deli baseline conditions. 

Sanitation-Related Scenarios 

Sanitation practices were a key driver in reducing the predicted risk of listeriosis. When sanitation 

activities were not modeled (“No Sanitation”), the predicted increase in risk could be as much as 50.2% 

(i.e., under retail deli conditions in which there were niches of L. monocytogenes and lack of temperature 

control to prevent growth; see “Niche & Temp. Control” column). The smallest predicted increase in risk 

from omitting sanitation was 2.9% (“Incoming Growth Chub” column); in this scenario, the impact of 

sanitation was overwhelmed by the additional bacteria from the incoming product and the potential 

growth of L. monocytogenes while the product was in the retail deli. While no individual changes in 

sanitation practices appear to substantively reduce the relative risk of listeriosis per serving of RTE food 

sliced or prepared in retail delis for all baselines, the substantive increase in risk (up to 50%) when 

sanitation is omitted is an indicator of the importance of sanitation at retail. 

Worker Behavior-Related Scenarios 

The impact of QRA simulated changes to worker behavior on the change in predicted relative risk varied, 

depending on the baseline retail deli condition. For example, if the retail deli had multiple niches 

(“Multiple Niche 100W), using separate slicers reduced the predicted relative risk by 6.3%. If workers did 

not use gloves, the predicted relative risk increased (5.1 to 8.5 %). In other cases, the public health

benefits of some interventions appear to have been overwhelmed by other factors. For example the benefit 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

130 

of pre-slicing product in the morning, after cleaning, was offset when incoming RTE products that 

support the growth of L. monocytogenes were highly contaminated. 

Growth Inhibitor-Related Scenarios 

Of all the scenarios tested, growth-inhibitor usage had the greatest impact on the predicted relative risk.

The use of growth inhibitors in all products almost completely eliminated the predicted relative risk 

(reductions ranged from 94.4 to 97.5%). This level of predicted relative risk reduction (approximately 

95%) is a significant finding, given that a 100% reduction would indicate no risk. In practice, however, 

not all products are amenable to incorporating growth inhibitors; therefore these results represent upper 

bounds in potential effectiveness. 

The baseline scenarios consider that products in the retail deli are a mixture of products that include 

growth inhibitors and products that do not. In a comparison scenario in which no products in the retail 

deli contained growth inhibitors (“No GI”), the predicted risk nearly doubled to between 184.1 and 

191.5%, compared with the baselines. The only apparent exception is in the “Incoming Growth Chub” 

baseline, where the estimated relative increase in risk was only 35.1%. This relatively low value is 

somewhat misleading, because, as mentioned above (see Table 18), the predicted absolute risk for this 

baseline was already almost 10 times higher than for other baselines.

These findings illustrate the importance of the growth of L. monocytogenes during retail and home storage 

for RTE products that support its growth. 

Cross-Contamination-Related Scenarios 

Table 19 shows that controlling cross contamination in retail delis is important in mitigating the risk of 

listeriosis. In the QRA scenarios in which cross contamination did not occur in the retail deli (i.e., the 

transfer coefficient for all sites and slicer were set to 0; see “Transfers and Slicer to 0” column), the 

predicted relative risk reduction was significant (ranging from 9.5% and 60.8%). However, when 

L. monocytogenes transfers from the slicer were not eliminated (“Transfers to 0” scenario), there was no 

significant reduction in the predicted relative risk. This highlights the importance of the slicer in retail 

delis as a primary source of cross contamination. 

In addition to examining the relative risk values in Table 19, examining the absolute risk estimates 

reported in Section 7.2 of this report provides further insight regarding the role cross contamination plays 

in the risk from RTE products prepared in retail delis. The absolute risk for a “No Niche” baseline when 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

131 

cross contamination is eliminated (i.e., “Transfer and Slicer to 0”) is 1.1 × 10-7 (see Figure 24). The 

absolute risk increases to 1.4 × 10-7 when there is cross contamination (see Table 18, “No Niche”). When 

an “Incoming Non-growth Chub” is introduced to a “No Niche” retail deli where there is no cross 

contamination, the risk remains essentially the same as the “No Niche, Transfer and Slicer to 0” scenario 

(1.1 × 10-7; Figure 34), indicating that if there is no cross contamination at retail, then the introduction of 

highly contaminated incoming RTE that does not support growth of L. monocytogenes does not result in 

any substantive increase in risk. However, when cross contamination does occur in these situations, the 

predicted absolute risk significantly increases (to 2.8 × 10-7; Table 18). This QRA illustrates that any 

increase in L. monocytogenes on incoming RTE product (even those that do not support its growth) 

increases the predicted risk of listeriosis on a per-serving basis as a result of cross contamination. 

It is of interest that Table 19 also showed that reducing the mean incoming L. monocytogenes levels in all 

RTE foods by a factor of 2 (0.3 log10 units) significantly reduced the predicted relative risk (between 1.1 

and 24.2%; see “Reduce Level” scenario). This finding suggests that a continued effort to prevent even 

low levels of L. monocytogenes contamination during processing prevents illnesses from these products 

and other RTE foods. 

Scenarios Related to Storage Temperature and Duration Control 

Controlling the deli case temperature significantly reduced the predicted risk. For the scenario in which

the RTE foods were held at the recommended temperature (“Temp ≤5°C”), the predicted reduction in risk 

was roughly the same as the reduction associated with holding RTE foods at temperatures that completely 

prevents growth of L. monocytogenes (“No Growth, T=-5°C”). This is an important finding, because 

maintaining products in the deli display at a temperature recommended by the FDA Food Code prevents 

almost all additional risk linked to the bacterial growth in retail. These findings highlight the importance 

of temperature control at retail.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

132 

Table 19: Predicted percent change in risk of invasive listeriosis per serving of ready-to-eat food sliced or 

prepared and sold at retail delis for the susceptible population according to various scenarios, as estimated by 

the “L. monocytogenes in retail delicatessens” risk assessment model. 

(percent change in the risk relative to the respective baseline condition) 1

Scenario

Baseline Conditions2

Multiple 

Niche 

100W

No 

Niche

Incoming 

Growth 

Chub

Incoming 

Non-growth 

Chub

Temp. 

Control

Niche & 

Temp. 

Control

Sanitation Related Scenarios:

Wash & Sanitize: Increase the effectiveness of 

cleaning from simply washing to washing and 

sanitizing.

-1.6 1.7 -0.6 2.0 -1.3 -7.6*

Clean 8 Sporadic: Double the number of sites 

cleaned from 4 to 8. -4.2 -4.1* -0.7 -1.9 -0.5 1.3

No Sanitation: No wiping, washing, or sanitizing. 41.3* 7.9* 2.9* 23.5* 11.9* 50.2*

No Sporadic Cleaning: Clean as required by the 

2009 FDA Food Code, but no additional sporadic 

cleanings.

3.0 -3.0 -0.4 1.7 1.7 3.5

NFCS As FCS: Workers clean deli NFCSs at same 

rate as FCS. -3.0 0.7 -0.6 0.3 -5.4* 0.9

Worker Behavior Related Scenarios:

No Glove: Workers do not use gloves when serving 

customers. 5.1* 2.5 1.2 8.5* 6.0* 7.0*

Gloves Every Serving: Workers change gloves 

before every sale. 4.1 0.7 0.7 0.6 -0.2 0.6

No Contact Glove Case: Workers do not use their 

hands to open the deli case (e.g. if a floor switch is 

used).

-1.4 -3.4 -1.3 1.3 1.3 -0.3

Pre-slice: Workers pre-slice RTE products in the 

morning, after cleaning 6.0* 24.9* 49.5* -34.4* 19.2* 1.0

Separate Slicer: Workers use a separate slicer for 

RTE products that support growth of 

L. monocytogenes.

-6.3* -0.6 -1.7* 22.7* -0.8 4.6

Do Not Slice Onto Gloves: Workers collect the slices 

of RTE products on tissue paper rather than on 

his/her gloved hand.

1.9 1.0 0.2 3.8 -1.9 8.0*

Growth Inhibitor Related Scenarios: 

All GI: Reformulate all RTE products sold at the 

retail deli that would otherwise support 

L. monocytogenes growth to include growth 

inhibitors.

-96.0* -95.2* -97.5* -94.5* -94.4* -94.8*

No GI: Reformulate all RTE products that support 

L. monocytogenes growth that are sold at the retail 

deli to not include GI to restrict L. monocytogenes 

growth.

184.1* 191.5* 35.1* 190.5* 187.7* 188.9*
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

133 

Scenario

Baseline Conditions2

Multiple 

Niche 

100W

No 

Niche

Incoming 

Growth 

Chub

Incoming 

Non-growth 

Chub

Temp. 

Control

Niche & 

Temp. 

Control

Cross Contamination Related Scenarios: 

Transfers to 0: Cross contamination results only 

from the deli slicer. -4.3 2.5 1.0 3.7 0.2 -0.3

Transfers and Slicer to 0: No cross contamination in 

the retail deli. -33.8* -18.6* -9.5* -60.8* -19.2* -30.4*

Reduce Level: Mean incoming L. monocytogenes

log10 concentration in all RTE products lowered 

from -9.2 to -9.5 log10 cfu/g.

-21.6* -24.2* -1.1 -9.8* -22.5* -15.6*

Separate Slicer Case: Workers use a separate slicer 

and a separate deli case for RTE products that support 

the growth of L. monocytogenes.

-2.5 -1.6 -1.2 21.0* -0.9 7.5*

Lower Env Cont: Reduce transfer of 

L. monocytogenes among RTE products, FCSs, and 

NFCs (i.e., reduce transfer coefficients by 50%).

-4.5 -4.4* -1.4 0.4 1.6 0.9

Storage Temperature and Duration Control Related Scenarios:

Temp = 5°C: Set the retail deli case temperature to 

5°C (41°F) (i.e., in compliance with the 2009 FDA 

Food Code) for all delis, instead of using the deli case 

temperatures reported by Ecosure.

-4.8 -14.3* -8.1* -2.8 NA NA

No Growth (T=-5°C): At this temperature, no 

L. monocytogenes growth will occur. -16.5* -21.3* -18.2* -5.7* NA NA

Temp ≤ 5°C: Use only the retail deli case 

temperatures observed in the Ecosure dataset at or 

below 5°C (41°F). 

-9.0* -16.3* -12.3* -8.2* NA NA

Shorten Time in Retail Delis: Reduce the length of 

time RTE products are held before they are sold or 

disposed from 7 to 4 days.

-2.5 3.3 -1.2 2.0 -0.2 1.7

Readers should refer to the body of the document for further details on the assumptions, model, data, baselines, and scenarios. 

See Table 18 for the predicted absolute risk of the baseline conditions.

* bold: Outside the 95% confidence interval for the median. 

NFCS = non-food-contact surface; FCS = food-contact surface; Temp. = Temperature; NA= not applicable to this scenario; Chub 

refers to bulk product (deli meat or cheese) before it is sliced. 

1

The US population was split in two subpopulations for the purposes of this risk assessment: the susceptible population (e.g., 

older adults, fetuses, newborns, and people with immune-compromising conditions, according to FAO/WHO 2004 definition) 

and the remaining population. The results for the susceptible population only are presented, because this population comprises 

80-98% of the public health burden for listeriosis 2

Description of the baseline conditions: Multiple Niche 100W = a retail deli with multiple niches that releases L. monocytogenes

to food-contact surfaces at a rate of 100 cfu on an average weekly frequency; No Niche = a retail deli with no niches or 

environmental L. monocytogenes transfer; Incoming Growth Chub = A retail deli with no niche or environmental 

L. monocytogenes transfer, with one incoming RTE product that is contaminated at levels higher than those of other products in 

the deli (mean of the log10: -5 log10 cfu/g vs -9.2 log10 cfu/g) and that does support the growth of L. monocytogenes; Incoming 

Non-growth Chub = A retail deli with no niche or environmental L. monocytogenes transfer, with one incoming RTE product 

that is contaminated at levels higher than those of other products in the deli (mean of the log10: -5 log10 cfu/g vs -9.2 log10 cfu/g)

and does not support the growth of L. monocytogenes; Niche & Temperature Control = a retail deli with multiple niches and 

compliant with the 2009 FDA Food Code guidance for temperature control (≤41°F): Temperature Control = a retail deli 

without any niches and with compliant temperature control.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

134 

9. Conclusions

This QRA represents the first large-scale effort to model L. monocytogenes cross contamination at retail. 

The risk assessment model contributes to our understanding of L. monocytogenes transmission, survival, 

and growth in the retail environment and was used to evaluate how retail practices may impact the 

predicted risk of listeriosis. The approach used was to evaluate the public health effect of various changes 

in practices under six different baseline conditions that may characterize a retail deli and the RTE food it 

serves. 

The key findings from this assessment of listeriosis risk associated with RTE foods prepared and served 

in retail deli operations include: 

• Control Growth. Employing practices that prevent bacterial growth dramatically reduced the 

predicted risk of listeriosis, as observed in other L. monocytogenes risk assessments. The use of 

growth inhibitors for suitable products prevents growth of L. monocytogenes in RTE foods both 

at retail and during consumer home storage. In this risk assessment, use of growth inhibitors led

to an overall dramatic reduction in the predicted risk of listeriosis (ca. 95%, see table). The strict 

control of temperature during refrigerated storage in retail delis did reduce the predicted risk. The 

impact of this control is nevertheless lower as it reduces growth only during this specific storage 

(5-20% reduction according to the baseline and the scenario) (see “Temperature Control” baseline 

and growth inhibitor scenarios).

• Control Cross Contamination. Cross contamination of L. monocytogenes in the retail 

environment dramatically increases the predicted risk of listeriosis. Cross contamination during 

the routine operation of the retail deli is not amenable to a simple solution (cf. “Transfers and 

Slicer to 0” scenarios). 

• Control Contamination at Its Source. Increasing the concentration and transfers of 

L. monocytogenes from incoming products, the environment, or niches directly increases the 

predicted risk of illness. Increasing L. monocytogenes concentration in incoming product 

increased the predicted risk of listeriosis whether or not the contaminated RTE product itself 

supported growth. The increase in predicted risk was greater when the equivalent contamination 

occurred on product that supported the growth of L. monocytogenes (cf. predicted risks for 

“Incoming Growth Chub” baseline and “Incoming Non-growth Chub” baseline, as well as 

“Reduce Level” scenarios). 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

135 

• Continue Sanitation. Sanitation practices that eliminate L. monocytogenes from deli FCS results 

in a reduction in the predicted risk of illness. Cleaning and sanitizing FCS reduced the predicted 

L. monocytogenes levels in the deli area (see “No Sanitation” scenario). Wearing gloves while 

serving customers reduces the estimated risk of listeriosis.

• Identify Key Routes of Contamination. The slicer is a primary source of L. monocytogenes

cross contamination to deli meats and cheeses. Control of L. monocytogenes cross contamination 

at this point during retail preparation reduced the predicted risk of listeriosis (see “Transfers to 0” 

versus “Transfers and Slicer to 0” scenarios). 

In summary, this risk assessment improves our understanding of L. monocytogenes in the retail deli and is 

intended to encourage improvements to retail food safety practices and mitigation strategies to further 

control L. monocytogenes in RTE foods. The “what if” scenarios modeled in this risk assessment provide 

insight on how cross contamination, sanitary practices, and temperature control impact the predicted risk 

of listeriosis. This risk assessment is based on an extensive amount of information gathered through 

partnerships with academia and input from stakeholders. Additional data would be useful, to refine and 

improve the predictions made by this “Virtual Deli” model and to further explore how more specific retail 

practices and conditions (e.g., equipment design) impact the risk of listeriosis. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

136 

References

1. Federal Food Safety Working Group, The Federal Food Safety Working Group Progress Report, 

2011. p. 31.Available from: http://www.whitehouse.gov/sites/default/files/fswg_report_final.pdf. 

2. Scallan, E., Hoekstra, R.M., Angulo, F.J., Tauxe, R.V., Widdowson, M.A., Roy, S.L., Jones, J.L., 

and Griffin, P.M., Foodborne illness acquired in the United States—major pathogens. Emerging 

Infectious Diseases, 2011. 17(1): p. 7-12. 

3. FDA/FSIS, Quantitative assessment of relative risk to public health from foodborne Listeria 

monocytogenes among selected categories of ready-to-eat foods, 2003, Food and Drug 

Administration, United States Department of Agriculture, Centers for Disease Control and 

Prevention. p. 541.Available from: 

http://www.fda.gov/Food/FoodScienceResearch/RiskSafetyAssessment/ucm183966.htm. 

4. FDA. Current FDA Activities Related to the Listeria monocytogenes. Action Plan February 6, 

2008. 2008 [cited 2012 June, 14]; Available from: 

http://www.fda.gov/Food/FoodScienceResearch/RiskSafetyAssessment/ucm208995.htm. 

5. FSIS, FSIS Risk Assessment for Listeria monocytogenes in deli meats, 2003, FSIS.Available 

from: http://www.fsis.usda.gov/PDF/Lm_Deli_Risk_Assess_Final_2003.pdf. 

6. FSIS, FSIS Comparative Risk Assessment for Listeria monocytogenes in Ready-to-eat Meat and 

Poultry Deli Meats, 2010: Washington, DC. p. 58.Available from: 

http://www.fsis.usda.gov/PDF/Comparative_RA_Lm_Report_May2010.pdf. 

7. FSIS, Compliance Guideline: Controlling Listeria monocytogenes in Post-Lethality Exposed 

Ready-to-Eat Meat and Poultry Products. [April 2006/revised Nov. 2012; Draft for public 

comment through November 16, 2012, 2012. 

8. FSIS. The FSIS Microbiological Testing Program for Ready-to-Eat (RTE) Meat and Poultry 

Products, 1990–2011. 2012 [cited 2013 March, 26]; Available from: 

http://www.fsis.usda.gov/Science/Micro_Testing_RTE/. 

9. CDC. Incidence of laboratory-confirmed bacterial and parasitic infections in 2009† and 

postdiarrheal hemolytic uremic syndrome (HUS) in 2008, by year and pathogen, Foodborne 

Diseases Active Surveillance Network (FoodNet), United States. 2009 [cited 2013 March, 26]; 

Available from: http://www.cdc.gov/foodnet/factsandfigures/2009/Table1b_all_incidence_96-

09.pdf. 

10. Silk, B.J., Date, K.A., Jackson, K.A., Pouillot, R., Holt, K.G., Graves, L.M., Ong, K.L., Hurd, S., 

Meyer, R., Marcus, R., Shiferaw, B., Norton, D.M., Medus, C., Zansky, S.M., Cronquist, A.B., 

Henao, O.L., Jones, T.F., Vugia, D.J., Farley, M.M., and Mahon, B.E., Invasive listeriosis in the 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

137 

Foodborne Diseases Active Surveillance Network (FoodNet), 2004-2009: further targeted 

prevention needed for higher-risk groups. Clinical Infectious Diseases, 2012. 54 Suppl 5: p. 

S396-404. 

11. CDC, Preliminary FoodNet Data on the Incidence of Infection with Pathogens Transmitted 

Commonly Through Food --- 10 States, 2009. Morbidity and Mortality Weekly Report, 2010. 

59(14): p. 418-422. 

12. CDC. CDC Wonder, the Healthy People 2010 Database. 2011 [cited 2013 March, 26]; Available 

from: http://wonder.cdc.gov/data2010/focus.htm. 

13. CDC. Trends in Foodborne Illness in the United States. 2013 [cited 2013 March, 26]; Available 

from: http://www.cdc.gov/foodborneburden/trends-in-foodborne-illness.html. 

14. Gombas, D.E., Chen, Y., Clavero, R.S., and Scott, V.N., Survey of Listeria monocytogenes in 

ready-to-eat foods. Journal of Food Protection, 2003. 66(4): p. 559-69. 

15. Draughon, A.F. A collaborative analysis/risk assessment of Listeria monocytogenes in ready-toeat processed meat and poultry collected in four FoodNet states. in International Association for 

Food Protection 93rd Annual Meeting. 2006. Calgary, Alberta, Canada. 

16. Endrikat, S., Gallagher, D., Pouillot, R., Hicks Quesenberry, H., Labarre, D., Schroeder, C.M., 

and Kause, J., A Comparative Risk Assessment for Listeria monocytogenes in Prepackaged 

versus Retail-Sliced Deli Meat. Journal of Food Protection, 2010. 73(4): p. 612-9. 

17. Pradhan, A.K., Ivanek, R., Grohn, Y.T., Bukowski, R., Geornaras, I., Sofos, J.N., and Wiedmann, 

M., Quantitative Risk Assessment of Listeriosis-Associated Deaths Due to Listeria 

monocytogenes Contamination of Deli Meats Originating from Manufacture and Retail. Journal 

of Food Protection, 2010. 73(4): p. 620-30. 

18. FDA, FDA Report on the Occurrence of Foodborne Illness Risk Factors in Selected Institutional 

Foodservice, Restaurant, and Retail Food Store Facility Types (2009), 2010.Available from: 

http://www.fda.gov/downloads/Food/FoodSafety/RetailFoodProtection/FoodborneIllnessandRisk

FactorReduction/RetailFoodRiskFactorStudies/UCM224682.pdf. 

19. EcoSure. 2007 U.S. Cold Temperature Evaluation: Design and Summary Pages. 2008 [cited 

2008 June, 4]; Available from: http://foodrisk.org/exclusives/EcoSure/. 

20. FDA, FDA Trend Analysis Report on the Occurrence of Foodborne Illness Risk Factors in 

Selected Institutional Foodservice, Restaurant, and Retail Food Store Facility Types (1998 – 

2008), 2010. p. 156.Available from: 

http://www.fda.gov/downloads/Food/FoodSafety/RetailFoodProtection/FoodborneIllnessandRisk

FactorReduction/RetailFoodRiskFactorStudies/UCM224152.pdf. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

138 

21. Perez-Rodriguez, F., Valero, A., Todd, E.C., Carrasco, E., Garcia-Gimeno, R.M., and Zurera, G., 

Modelling transfer of Escherichia coli O157:H7 and Staphylococcus aureus during slicing of a 

cooked meat product. Meat Science, 2007. 76(4): p. 692-699. 

22. Vorst, K.L., Todd, E.C., and Ryser, E.T., Transfer of Listeria monocytogenes during slicing of 

turkey breast, bologna, and salami with simulated kitchen knives. Journal of Food Protection, 

2006. 69(12): p. 2939-46. 

23. Vorst, K.L., Todd, E.C., and Rysert, E.T., Transfer of Listeria monocytogenes during mechanical 

slicing of turkey breast, bologna, and salami. Journal of Food Protection, 2006. 69(3): p. 619-26. 

24. Sauders, B.D., Sanchez, M.D., Rice, D.H., Corby, J., Stich, S., Fortes, E.D., Roof, S.E., and 

Wiedmann, M., Prevalence and molecular diversity of Listeria monocytogenes in retail 

establishments. Journal of Food Protection, 2009. 72(11): p. 2337-49. 

25. Hoelzer, K., Sauders, B.D., Sanchez, M.D., Olsen, P.T., Pickett, M.M., Mangione, K.J., Rice, 

D.H., Corby, J., Stich, S., Fortes, E.D., Roof, S.E., Grohn, Y.T., Wiedmann, M., and Oliver, H.F., 

Prevalence, distribution, and diversity of Listeria monocytogenes in retail environments, focusing 

on small establishments and establishments with a history of failed inspections. Journal of Food 

Protection, 2011. 74(7): p. 1083-95. 

26. Pradhan, A.K., Ivanek, R., Grohn, Y.T., Bukowski, R., and Wiedmann, M., Comparison of Public 

Health Impact of Listeria monocytogenes Product-to-Product and Environment-to-Product 

Contamination of Deli Meats at Retail. Journal of Food Protection, 2011. 74(11): p. 1860-8. 

27. FDA, Food Code 2009, 2009: College Park, MD, USA.Available from: 

http://www.fda.gov/downloads/Food/FoodSafety/RetailFoodProtection/FoodCode/FoodCode200

9/UCM189448.pdf. 

28. Lubran, M.B., Pouillot, R., Bohm, S., Calvey, E.M., Meng, J., and Dennis, S., Observational 

Study of Food Safety Practices in Retail Deli Departments. Journal of Food Protection, 2010. 

73(10): p. 1849-1857. 

29. Maitland, J., Boyer, R., Gallagher, D., Duncan, S., Bauer, N., Kause, J., and Eifert, J., Tracking 

Cross Contamination Transfer Dynamics at a Mock Retail Deli Market using GloGermTM.

Journal of Food Protection, 2013. 76(2): p. 272-282. 

30. Gibson, K.E., Koo, O.K., O'Bryan, C.A., Neal, J.A., Ricke, S.C., and Crandall, P.G., Observation 

and relative quantification of cross-contamination within a mock retail delicatessen environment.

Food Control, 2013. 31(1): p. 116-124. 

31. Hoelzer, K., Oliver, H.F., Kohl, L.R., Hollingsworth, J., Wells, M.T., and Wiedmann, M., 

Structured expert elicitation about Listeria monocytogenes cross-contamination in the 

environment of retail deli operations in the United States. Risk analysis, 2012. 32(7): p. 1139-56. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

139 

32. American Chemical Society, Understanding Risk Analysis: A Short Guide for Health, Safety, and 

Environmental Policy Making, 1998, American Chemical Society: Washington, DC.Available 

from: http://www.rff.org/rff/publications/upload/14418_1.pdf. 

33. Ivanek, R., Grohn, Y.T., Wiedmann, M., and Wells, M.T., Mathematical model of Listeria 

monocytogenes cross-contamination in a fish processing plant. Journal of Food Protection, 2004. 

67(12): p. 2688-97. 

34. Mylius, S.D., Nauta, M.J., and Havelaar, A.H., Cross-contamination during food preparation: a 

mechanistic model applied to chicken-borne Campylobacter. Risk analysis, 2007. 27(4): p. 803-

13. 

35. Schaffner, D.W., Mathematical frameworks for modelling Listeria cross-contamination in foodprocessing plants. Journal of Food Science, 2004. 69(6): p. R155-R159. 

36. Mokhtari, A. and Jaykus, L.A., Quantitative exposure model for the transmission of norovirus in 

retail food preparation. International Journal of Food Microbiology, 2009. 133(1-2): p. 38-47. 

37. Hoelzer, K., Pouillot, R., Gallagher, D., Silverman, M.B., Kause, J., and Dennis, S., Estimation of 

Listeria monocytogenes transfer coefficients and efficacy of bacterial removal through cleaning 

and sanitation. International Journal of Food Microbiology, 2012. 157(2): p. 267-277. 

38. Codex alimentarius Commission, Principles and guidelines for the conduct of microbiological 

risk assessment, 1999, FAO edition: Rome. p. 6.Available from: 

http://www.codexalimentarius.net/download/standards/357/CXG_030e.pdf. 

39. European Commission, Updated Opinion of the Scientific Steering Committee on Harmonisation 

of Risk Assessment Procedures, 2003, EC, . p. 5.Available from: 

http://ec.europa.eu/food/fs/sc/ssc/out355_en.pdf. 

40. Frey, H.C., Quantitative Analysis of Uncertainty and Variability in Environmental Policy 

Making, 1992, American Association for the Advancement of Science / U.S. Environmental 

Protection Agency.Available from: www4.ncsu.edu/~frey/reports/frey_92.pdf. 

41. Nauta, M., The Modular Process Risk Model (MPRM): a structured approach to food chain 

exposure assessment, in Microbial Risk Analysis of Foods, Schaffner, D.W., Editor 2008, ASM 

Press: Washington, D.C. p. 99-136. 

42. Nauta, M.J., A modular process risk model structure for quantitative microbiological risk 

assessment and its application in an exposure assessment of Bacillus Cereus in a REPFED, 2001, 

RIVM: Bilthoven.Available from: http://www.rivm.nl/bibliotheek/rapporten/149106007.pdf. 

43. Aziza, F., Mettler, E., Daudin, J.J., and Sanaa, M., Stochastic, compartmental, and dynamic 

modeling of cross-contamination during mechanical smearing of cheeses. Risk analysis, 2006. 

26(3): p. 731-745. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

140 

44. Keeratipibul, S. and Lekroengsin, S., Risk assessment of Listeria spp. contamination in the 

production line of ready-to-eat chicken meat products. Journal of Food Protection, 2008. 71(5): 

p. 946-52. 

45. den Aantrekker, E.D., Recontamination in food processing: quantitative modelling for risk 

assessment, 2002, Wageningen University. p. 1-128. 

46. den Aantrekker, E.D., Beumer, R.R., van Gerwen, S.J., Zwietering, M.H., van, S.M., and Boom, 

R.M., Estimating the probability of recontamination via the air using Monte Carlo simulations.

International Journal of Food Microbiology, 2003. 87(1-2): p. 1-15. 

47. Kusumaningrum, H.D., van Asselt, E.D., Beumer, R.R., and Zwietering, M.H., A quantitative 

analysis of cross-contamination of Salmonella and Campylobacter spp. via domestic kitchen 

surfaces. Journal of Food Protection, 2004. 67(9): p. 1892-903. 

48. Yang, H., Mokhtari, A., Jaykus, L.A., Morales, R.A., Cates, S.C., and Cowen, P., Consumer 

phase risk assessment for Listeria monocytogenes in deli meats. Risk analysis, 2006. 26(1): p. 89-

103. 

49. Perez-Rodriguez, F., Valero, A., Carrasco, E., Garcia, R.M., and Zurera, G., Understanding and 

modelling bacterial transfer to foods: a review. Trends in Food Science & Technology, 2008. 

19(3): p. 131-144. 

50. Montville, R. and Schaffner, D.W., Inoculum size influences bacterial cross contamination 

between surfaces. Applied and Environmental Microbiology, 2003. 69(12): p. 7188-93. 

51. Fravalo, P., Laisney, M.-J., Gillard, M.-O., Salvat, G., and Chemaly, M., Campylobacter transfer 

from naturally contaminated chicken thighs to cutting boards is inversely related to initial load.

Journal of Food Protection, 2009. 72(5): p. 1836-40. 

52. Rodriguez, A., Autio, W.R., and McLandsborough, L.A., Effects of inoculation level, material 

hydration, and stainless steel surface roughness on the transfer of Listeria monocytogenes from 

inoculated bologna to stainless steel and high-density polyethylene. Journal of Food Protection, 

2007. 70(6): p. 1423-8. 

53. Nauta, M.J., "Campylobacter transfer from naturally contaminated chicken thighs to cutting 

boards is inversely related to initial load," a comment on: J. Food Prot. 72(9): 1836-1840 (2009).

Journal of Food Protection, 2010. 73(1): p. 6-7; author reply 7-8. 

54. Sheen, S. and Hwang, C.A., Modeling transfer of Listeria monocytogenes from slicer to deli meat 

during mechanical slicing. Foodborne Pathogens and Disease, 2008. 5(2): p. 135-46. 

55. Aarnisalo, K., Sheen, S., Raaska, L., and Tamplin, M., Modelling transfer of Listeria 

monocytogenes during slicing of 'gravad' salmon. International Journal of Food Microbiology, 

2007. 118(1): p. 69-78. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

141 

56. Keskinen, L.A., Todd, E.C., and Ryser, E.T., Transfer of surface-dried Listeria monocytogenes

from stainless steel knife blades to roast turkey breast. Journal of Food Protection, 2008. 71(1): 

p. 176-81. 

57. Keskinen, L.A., Todd, E.C., and Ryser, E.T., Impact of bacterial stress and biofilm-forming 

ability on transfer of surface-dried Listeria monocytogenes during slicing of delicatessen meats.

International Journal of Food Microbiology, 2008. 127(3): p. 298-304. 

58. Sheen, S., Modeling surface transfer of Listeria monocytogenes on salami during slicing. Journal 

of Food Science, 2008. 73(6): p. E304-E311. 

59. FAO/WHO, Risk assessment of Listeria monocytogenes in ready to eat foods - Technical report, 

in Microbiological Risk Assessment Series, no 52004, Food and Agriculture Organization of the 

United Nations and World Health Organization: Rome. p. 269.Available from: 

http://www.fao.org/docrep/010/y5394e/y5394e00.htm. 

60. Ross, T. and McMeekin, T.A., Modeling microbial growth within food safety risk assessments.

Risk analysis, 2003. 23(1): p. 179-97. 

61. van Gerwen, S.J.C. and Zwietering, M.H., Growth and inactivation models to be used in 

quantitative risk assessments. Journal of Food Protection, 1998. 61(11): p. 1541-1549. 

62. Buchanan, R.L., Whiting, R.C., and Damert, W.C., When is simple good enough: a comparison 

of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves. Food 

Microbiology, 1997. 14(4): p. 313-326. 

63. Yule, G.U., The growth of population and the factors which control it. Journal of the Royal 

Statistical Society: Series B, 1925. 25(1): p. 1-58. 

64. Vose, D., Risk Analysis: a quantitative guide. third edition ed2008, Chichester, UK: Wiley and 

Sons. 

65. Zwietering, M.H., de Wit, J.C., and Notermans, S., Application of predictive microbiology to 

estimate the number of Bacillus cereus in pasteurised milk at the point of consumption.

International Journal of Food Microbiology, 1996. 30(1-2): p. 55-70. 

66. Augustin, J.C., Zuliani, V., Cornu, M., and Guillier, L., Growth rate and growth probability of 

Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions. Journal of 

Applied Microbiology, 2005. 99(5): p. 1019-42. 

67. Ross, T., Rasmussen, S., Fazil, A., Paoli, G., and Summer, J., Quantitative risk assessment of 

Listeria monocytogenes in ready-to-eat meats in Australia. International Journal of Food 

Microbiology, 2009. 131(2-3): p. 128-37. 

68. Ratkowsky, D.A., Olley, J., McMeekin, T.A., and Ball, T.A., Relationship between temperature 

and growth rate of bacterial cultures. Journal of Bacteriology, 1982. 149(1): p. 1-5.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

142 

69. Zwietering, M.H., de Koos, J.T., Hasenack, B.E., de Witt, J.C., and van't Riet, K., Modeling of 

bacterial growth as a function of temperature. Applied and Environmental Microbiology, 1991. 

57(4): p. 1094-101. 

70. Presser, K.A., Ratkowsky, D.A., and Ross, T., Modelling the growth rate of Escherichia coli as a 

function of pH and lactic acid concentration. Applied and Environmental Microbiology, 1997. 

63(6): p. 2355-2360. 

71. Le Marc, Y., Huchet, V., Bourgeois, C.M., Guyonnet, J.P., Mafart, P., and Thuault, D., Modelling 

the growth kinetics of Listeria as a function of temperature, pH and organic acid concentration.

International Journal of Food Microbiology, 2002. 73(2-3): p. 219-37. 

72. Devlieghere, F., Geeraerd, A.H., Versyck, K.J., Vandewaetere, B., Van Impe, J., and Debevere, 

J., Growth of Listeria monocytogenes in modified atmosphere packed cooked meat products: a 

predictive model. Food Microbiology, 2001. 18(1): p. 53-66. 

73. Tienungoon, S., Ratkowsky, D.A., McMeekin, T.A., and Ross, T., Growth limits of Listeria 

monocytogenes as a function of temperature, pH, NaCl, and lactic acid. Applied and 

Environmental Microbiology, 2000. 66(11): p. 4979-87. 

74. Gimenez, B. and Dalgaard, P., Modelling and predicting the simultaneous growth of Listeria 

monocytogenes and spoilage micro-organisms in cold-smoked salmon. Journal of Applied 

Microbiology, 2004. 96(1): p. 96-109. 

75. Mejlholm, O. and Dalgaard, P., Development and Validation of an extensive growth and growth 

boundary model for Listeria monocytogenes in lightly preserved and ready-to-eat shrimp. Journal 

of Food Protection, 2009. 72(10): p. 2132-2143. 

76. Ross, T., Indices for performance evaluation of predictive models in food microbiology. Journal 

of Applied Bacteriology, 1996. 81(5): p. 501-8. 

77. Wederquist, H.J., Sofos, J.N., and Schmidt, G.R., Listeria monocytogenes inhibition in 

refrigerated vacuum-packaged turkey bologna by chemical additives. Journal of Food Science, 

1994. 59(3): p. 498-500. 

78. Blom, H., Nerbrink, E., Dainty, R., Hagtvedt, T., Borch, E., Nissen, H., and Nesbakken, T., 

Addition of 2.5% lactate and 0.25% acetate controls growth of Listeria monocytogenes in 

vacuum-packed, sensory-acceptable servelat sausage and cooked ham stored at 4°C. International 

Journal of Food Microbiology, 1997. 38(1): p. 71-76. 

79. Glass, K., Preston, D., and Veesenmeyer, J., Inhibition of Listeria monocytogenes in turkey and 

pork-beef bologna by combinations of sorbate, benzoate, and propionate. Journal of Food 

Protection, 2007. 70(1): p. 214-217. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

143 

80. Glass, K.A., McDonnell, L.M., Rassel, R.C., and Zierke, K.L., Controlling Listeria 

monocytogenes on sliced ham and turkey products using benzoate, propionate, and sorbate.

Journal of Food Protection, 2007. 70(10): p. 2306-2312. 

81. Delignette-Muller, M.L., Cornu, M., Pouillot, R., and Denis, J.B., Use of Bayesian modelling in 

risk assessment: Application to growth of Listeria monocytogenes and food flora in cold-smoked 

salmon. International Journal of Food Microbiology, 2006. 106(2): p. 195-208. 

82. Zuliani, V., Lebert, I., Augustin, J.C., Garry, P., Vendeuvre, J.L., and Lebert, A., Modelling the 

behaviour of Listeria monocytogenes in ground and pork as a function of pH, water activity, 

nature and concentration of organic acid salts. Journal of Applied Microbiology, 2007. 103(3): p. 

536-550. 

83. Mejlholm, O., Gunvig, A., Borggaard, C., Blom-Hanssen, J., Mellefont, L., Ross, T., Leroi, F., 

Else, T., Visser, D., and Dalgaard, P., Predicting growth rates and growth boundary of Listeria 

monocytogenes – an international validation study with focus on processed and ready-to-eat meat 

and seafood. International Journal of Food Microbiology, 2010. 141(3): p. 137-150. 

84. Augustin, J.C., Bergis, H., Midelet-Bourdin, G., Cornu, M., Couvert, O., Denis, C., Huchet, V., 

Lemonnier, S., Pinon, A., Vialette, M., Zuliani, V., and Stahl, V., Design of challenge testing 

experiments to assess the variability of Listeria monocytogenes growth in foods. Food 

Microbiology, 2011. 28(4): p. 746-54. 

85. FAO/WHO, Exposure assessment of microbiological hazards in food, in Microbiological Risk 

Assessment Series, no 72008, Food and Agriculture Organization of the United Nations and 

World Health Organization: Rome. p. 92. 

86. Ellouze, M., Gauchi, J.P., and Augustin, J.C., Global sensitivity analysis applied to a 

contamination assessment model of Listeria monocytogenes in cold smoked salmon at 

consumption. Risk analysis, 2010. 30(5): p. 841-52. 

87. Pouillot, R., Goulet, V., Delignette-Muller, M.L., Mahe, A., and Cornu, M., Quantitative Risk 

Assessment of Listeria monocytogenes in French cold-smoked salmon: II. Risk characterization.

Risk analysis, 2009. 29(6): p. 806-819. 

88. Todd, E.C.D., Greig, J.D., Bartleson, C.A., and Michaels, B.S., Outbreaks where food workers 

have been implicated in the spread of foodborne disease. Part 6. Transmission and survival of 

pathogens in the food processing and preparation environment. Journal of Food Protection, 2009. 

72: p. 202-219. 

89. Kosa, K.M., Cates, S.C., Karns, S., Godwin, S.L., and Chambers, D., Consumer home 

refrigeration practices: results of a web-based survey. Journal of Food Protection, 2007. 70(7): p. 

1640-1649. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

144 

90. Pouillot, R., Lubran, M.B., Cates, S.C., and Dennis, S., Estimating parametric distributions of 

storage time and temperature of ready-to-eat foods for U.S. households. Journal of Food 

Protection, 2010. 73(2): p. 312-21. 

91. Haas, C.N., Rose, J.B., and Gerba, C.P., Quantitative microbial risk assessment1999, New York: 

Wiley.

92. FAO/WHO, Hazard characterization for pathogens in food and water. Guidelines, in 

Microbiological Risk Assessment Series, no 32003, Food and Agriculture Organization of the 

United Nations and World Health Organization: Rome. p. 269. 

93. Rocourt, J., Risk factors for listeriosis. Food Control, 1996. 7(4-5): p. 195-202. 

94. Goulet, V., Hebert, M., Hedberg, C., Laurent, E., Vaillant, V., De Valk, H., and Desenclos, J.C., 

Incidence of listeriosis and related mortality among groups at risk of acquiring listeriosis. Clinical 

Infectious Diseases, 2012. 54(5): p. 652-60. 

95. FDA/FSIS, Draft assessment of the relative risk to public health from foodborne Listeria 

monocytogenes among selected categories of ready-to-eat foods, 2001, Food and Drug 

Administration, United States Department of Agriculture, Centers for Disease Control and 

Prevention. p. 381. 

96. Mead, P.S., Slutsker, L., Dietz, V., McCaig, L.F., Bresee, J.S., Shapiro, C., Griffin, P.M., and 

Tauxe, R.V., Food-related illness and death in the United States. Emerging Infectious Diseases, 

1999. 5(5): p. 607-25. 

97. Mary Kay O’Connor, M.K., Hlebert, A., Johnson, J., Kingsburg, K., and Peckham, K., What’s in 

store 2010. , 2010, International dairy deli bakery association: Madison, WI.

98. FSIS, Isolation and Identification of Listeria monocytogenes from Red Meat, Poultry and Egg 

Products, and Environmental Samples, 2009: Washington, DC. p. 21.Available from: 

http://www.fsis.usda.gov/PDF/MLG-8.pdf. 

99. Gallagher, D., Ebel, E.D., Gallagher, O., Labarre, D., Williams, M.S., Golden, N.J., Pouillot, R., 

Dearfield, K.L., and Kause, J., Characterizing uncertainty when evaluating risk management 

metrics: Risk assessment modeling of Listeria monocytogenes contamination in ready-to-eat deli 

meats. International Journal of Food Microbiology, 2013. 162(3): p. 266-275. 

100. Barmpalia, I.M., Geornaras, I., Belk, K.E., Scanga, J.A., Kendall, P.A., Smith, G.C., and Sofos, 

J.N., Control of Listeria monocytogenes on frankfurters with antimicrobials in the formulation 

and by dipping in organic acid solutions. Journal of Food Protection, 2004. 67(11): p. 2456-64. 

101. Barmpalia, I.M., Koutsoumanis, K.P., Geornaras, I., Belk, K.E., Scanga, J.A., Kendall, P.A., 

Smith, G.C., and Sofos, J.N., Effect of antimicrobials as ingredients of pork bologna for Listeria 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

145 

monocytogenes control during storage at 4 or 10 degrees C. Food Microbiology, 2005. 22(2-3): p. 

205-211. 

102. Glass, K.A., Granberg, D.A., Smith, A.L., McNamara, A.M., Hardin, M., Mattias, J., Ladwig, K., 

and Johnson, E.A., Inhibition of Listeria monocytogenes by sodium diacetate and sodium lactate 

on wieners and cooked bratwurst. Journal of Food Protection, 2002. 65(1): p. 116-123. 

103. Houtsma, P.C., Kusters, B.J.M., de Wit, J.C., Rombouts, F.M., and Zwietering, M.H., Modelling 

growth rates of Listeria innocua as a function of lactate concentration. International Journal of 

Food Microbiology, 1994. 24(1-2): p. 113-123. 

104. Houtsma, P.C., de Wit, J.C., and Rombouts, F.M., Minimum inhibitory concentration (MIC) of 

sodium lactate for pathogens and spoilage organisms occurring in meat products. International 

Journal of Food Microbiology, 1993. 20(4): p. 247-257. 

105. Hwang, C.A. and Tamplin, M.L., Modeling the lag phase and growth rate of Listeria 

monocytogenes in ground ham containing sodium lactate and sodium diacetate at various storage 

temperatures. Journal of Food Science, 2007. 72(7): p. M246-M253. 

106. Jacobsen, T. and Koch, A.G., Influence of different histories of the inoculum on lag phase and 

growth of Listeria monocytogenes in meat models. Journal of Food Protection, 2006. 69(3): p. 

532-41. 

107. Lianou, A., Geornaras, I., Kendall, P.A., Belk, K.E., Scanga, J.A., Smith, G.C., and Sofos, J.N., 

Fate of Listeria monocytogenes in commercial ham, formulated with or without antimicrobials, 

under conditions simulating contamination in the processing or retail environment and during 

home storage. Journal of Food Protection, 2007. 70(2): p. 378-85. 

108. Maks, N., Zhu, L., Juneja, V.K., and Ravishankar, S., Sodium lactate, sodium diacetate and 

pediocin: Effects and interactions on the thermal inactivation of Listeria monocytogenes on 

bologna. Food Microbiology, 2010. 27(1): p. 64-69. 

109. Mbandi, E. and Shelef, L.A., Enhanced antimicrobial effects of combination of lactate and 

diacetate on Listeria monocytogenes and Salmonella spp. in beef bologna. International Journal 

of Food Microbiology, 2002. 76(3): p. 191-198. 

110. Samelis, J., Bedie, G.K., Sofos, J.N., Belk, K.E., Scanga, J.A., and Smith, G.C., Control of 

Listeria monocytogenes with combined antimicrobials after postprocess contamination and 

extended storage of frankfurters at 4 degrees C in vacuum packages. Journal of Food Protection, 

2002. 65(2): p. 299-307. 

111. Samelis, J., Sofos, J.N., Kain, M.L., Scanga, J.A., Belk, K.E., and Smith, G.C., Organic acids and 

their salts as dipping solutions to control Listeria monocytogenes inoculated following processing 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

146 

of sliced pork bologna stored at 4 degrees C in vacuum packages. Journal of Food Protection, 

2001. 64(11): p. 1722-1729. 

112. Thompson, R.L., Carpenter, C.E., Martini, S., and Broadbent, J.R., Control of Listeria 

monocytogenes in ready-to-eat meats containing sodium levulinate, sodium lactate, or a 

combination of sodium lactate and sodium diacetate. Journal of Food Science, 2008. 73(5): p. 

M239-44. 

113. Zhu, M.J., Mendonca, A., Ismail, H.A., Du, M., Lee, E.J., and Ahn, D.U., Impact of antimicrobial 

ingredients and irradiation on the survival of Listeria monocytogenes and the quality of ready-toeat turkey ham. Poultry Science, 2005. 84(4): p. 613-20. 

114. Pal, A., Labuza, T.P., and Diez-Gonzalez, F., Evaluating the growth of Listeria monocytogenes in 

refrigerated ready-to-eat frankfurters: influence of strain, temperature, packaging, lactate and 

diacetate, and background microflora. Journal of Food Protection, 2008. 71(9): p. 1806-16. 

115. Seman, D.L., Borger, A.C., Meyer, J.D., Hall, P.A., and Milkowski, A.L., Modeling the growth 

of Listeria monocytogenes in cured ready-to-eat processed meat products by manipulation of 

sodium chloride, sodium diacetate, potassium lactate, and product moisture content. Journal of 

Food Protection, 2002. 65(4): p. 651-8. 

116. Glass, K.A. and Doyle, M.P., Fate of Listeria monocytogenes in processed meat products during 

refrigerated storage. Applied and Environmental Microbiology, 1989. 55(6): p. 1565-9. 

117. Holley, R.A., Doyon, G., Fortin, J., Rodrigue, N., and Carbonneau, M., Post-process, packaginginduced fermentation of delicatessen meats. Food Research International, 1996. 29(1): p. 35-48. 

118. Ryser, E.T. and Marth, E.H., Behavior of Listeria monocytogenes during manufacture and 

ripening of brick cheese. Journal of Dairy Science, 1989. 72(4): p. 838-53. 

119. Genigeorgis, C., Carniciu, M., Dutulescu, D., and Farver, T.B., Growth and survival of Listeria 

monocytogenes in market cheeses stored at 4 degrees C to 30 degrees C. Journal of Food 

Protection, 1991. 54(9): p. 662-668. 

120. Refrigerated Foods Association, RE: Update of the 2003 Interagency Quantitative Assessment of 

the Relative Risk to Public Health From Foodborne Listeria Monocytogenes Among Selected 

Categories of Ready-To-Eat Foods; Request for Comments, Scientific Data and Information, 

FSIS-2010-0035, D.N., Editor 2011. 

121. R Development Core Team. R: a language and environment for statistical computing. 2008 [cited 

2012 March 20]; Available from: http://www.R-project.org. 

122. Riedo, F.X., Pinner, R.W., Tosca, M.L., Cartter, M.L., Graves, L.M., Reeves, M.W., Weaver, 

R.E., Plikaytis, B.D., and Broome, C.V., A point-source foodborne listeriosis outbreak: 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

147 

documented incubation period and possible mild illness. Journal of infectious Diseases, 1994. 

170(3): p. 693-6. 

123. Todd, E.C., Michaels, B.S., Greig, J.D., Smith, D., and Bartleson, C.A., Outbreaks where food 

workers have been implicated in the spread of foodborne disease. Part 8. Gloves as barriers to 

prevent contamination of food by workers. Journal of Food Protection, 2010. 73(9): p. 1762-73. 

124. Perez-Rodriguez, F., Castro, R., Posada-Izquierdo, G.D., Valero, A., Carrasco, E., GarciaGimeno, R.M., and Zurera, G., Evaluation of hygiene practices and microbiological quality of 

cooked meat products during slicing and handling at retail. Meat Science, 2010. 86(2): p. 479-85. 

125. Nims, L.F. and Smith, P.K., The Ionization of lactic acid from zero to fifty degrees. Journal of 

Biological Chemistry, 1936. 113: p. 145-152. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

148 

Appendix 1: The Secondary Growth Model

The Mejlholm and Dalgaard model [75] is a predictive microbiology secondary model. It predicts the 

change in the primary model parameters according to a change in the growth environment. This model 

uses the gamma concept [65]. The model used here is limited to T, pH, aw, nitrites, LAC, and DAC and 

their interaction. It is written: 

( ) ( ) ( ) ( ) ( ) ( ) ( T apH nit LAC DAC DAC w LACnitapHT DAC) Tref pH w nitwa LAC ,,,,, ξ int ⋅⋅⋅⋅⋅= γγγγγμμ ⋅ γ ⋅

where 

- 1 h419.0 − μ ref = is equal to μopt at a reference temperature (Tref) of 25°C in the RTE product; 

- ( ) 









°−≤

°−> 















+

+

=

if T C

if T C

T

T

T

ref T

0 83.2

83.2

83.2

83.2

2

γ with T, the temperature in degrees Celsius. 

Note that this model for temperature is equivalent to a Ratkowsky [68] model, as used in [3], with 

Tmin, the minimal temperature of growth for Listeria equals to -2.83°C (26.91°F); 

- ( )

( )

( ) 









≤

> −

−

=

0 923.0

923.0

923.01

923.0

w

w w

wa

if a

if a

a

a w γ with aw , the water activity of the RTE product; 

- ( ) ( )









≤

− > =

−

0 97.4

101 97.4 97.4

if pH

if pH pH

pH

γ pH with pH, the pH of the RTE product; 

- ( ) 









≥

 < 

 



 −

=

if nit ppm

if nit ppm

nit

nit nit

0 350

350

350

350 2

γ with nit the concentration of nitrites (ppm);

- ( ) [ ]

[ ] [ ]

 [ ] 







≥

 <



 



 − =

LACf mM

if LAC mM LAC

LAC

U

U

U

LAC U

0 79.3

79.3

79.3

1

γ with [LACU], the concentration (mM) 

of undissociated lactic acid.

- ( ) [ ]

[ ] [ ]

[ ] 









≥

< 













 − =

if DAC mM

if DAC mM DAC

DAC

U

U

U

DAC U

0 80.4

80.4

80.4

1

γ with [DACU], the concentration (mM) 

of undissociated diacetate. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

149 

The value of [LACU] from the concentration of growth inhibitors in the RTE product is evaluated in two 

steps: First, the total lactic acid concentration (LACtot, %) is evaluated from the concentration in sodium 

lactate (NaL, %), the concentration in potassium lactate (KL, %), and the concentration in lactic acid 

(LAC, %) using their respective molecular weight, as:





 



 ++×=

08.902.1281.112

08.90 NaL KL LAC LACtot . 

Then, the concentration in undissociated lactic acid (mM) is evaluated from the total concentration of 

lactic acid using the Henderson-Hasselbach equation: 

[ ]

[ ] U a A

A pKpH − += 10 log

leading to 

[ ] 86.3 101

08.90

10000

− +

×

= pH

tot

U

LAC

LAC ; 

The pKa dependence on temperature was found to be negligible for lactic acid [125] and assumed so for 

diacetate. Similarly, the total diacetate concentration (DACtot, %) is evaluated from the concentration in 

sodium lactate (NaDAC, %) and the concentration in diacetate (DAC, %) using their respective molecular 

weight, as: 





 





×= +

1.11909.142

1.119 NaDAC DAC DACtot . 

The concentration in undissociated diacetate (mM) is evaluated from the total concentration of diacetate 

using: 

[ ] 76.4 101

1.119

10000

− +

×

= pH

tot

U

DAC

DAC ; 

As for the interaction term, Mejlholm and Dalgaard [75] use the Le Marc [71] approach, i.e.: 

( [ ] [ ]) ( ) 









≥

− <<

≤

=

0 1

15.012

1 5.0

,,,,,

ψ

ψ ψ

ψ

ξ

if

if

if

w LACnitpHaT U DACU

where 

( )

( )( )

( ) ( )( )

( )( ) 























−

−

×=

























− =

∏

∑

∑ ∏

≠ i

i

i

ij

i

ii

j

i

φ

φφ

φ

φ ψ 1

1

5.0

12

with
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

150 

( )

2

83.2

83.2 1 















+

+ −=

Tref

T

φ T , 

( )

2

923.01

923.0 1 















−

− −= w w

a

φ a , 

( ) ( ) 2

97.4 1011 



 



 −−= − pH φ pH , 

( )

2

350

350 1 



 



 − −= nit

φ nit and 

([ ] [ ]) [ ] [ ] 2

80.4

1

79.3 ; 1 1 





























 −⋅ 













 −−= U U

Uu

LAC DAC

φ LAC DAC . 

The growth model was slightly adapted to fit the structure of the present model. Nevertheless, these 

adaptations were made with no change in the mathematical model. 

Simplification of the model 

We first simplify the final evaluation of ξ . We have:

( )

( ) ( )

( ) 









≥−

− <−<

≤−

=

1121

12 1120

0120

ψ

ψ ψ

ψ

ξ

. 

Define χ = 2(1 - ψ). We have: 

( )

∏( )

∑

−

−

−=

i

i

i

ii

φ

φφ

χ 1

1

2

Then we can rewrite ξ simply as:











≥

<<

≤

=

11

10

00

χ

χχ

χ

ξ

Mathematical derivation when only one parameter is varying

A given RTE product has a set of chemical characteristics (pH, aw, [LAC]tot, [DAC]tot, nit). We will 

consider those characteristics as constant throughout the process, from entry in the deli to consumption. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

151 

Only one parameter is considered: temperature T. The following procedure will help us evaluate the 

growth of the RTE product while the temperature varies in the process. 

The gamma concept with interaction is written, for i ∈ {T, pH, aw, nit, [LAC]tot, [DAC]tot}: 

( ) ( ) ( )×× ξγγμξγμμ 















×= ∏ ×=× ∏≠x Tx T

Ti

opt ii

i

opt ii . 

( )















×∏≠Ti

opt ii γμ x is characteristic of the RTE product. In our particular process, it is a constant.

We have:

( )

( )

( ) ( )

( ) ( ) T

Ti

i

TT

Ti

ii

i

i

i

ii

φφ

φφφφ

φ

φφ

χ

−×−

−+−

−= −

−

−=

∏

∑

∏

∑

≠

≠

11

1 1

2

1

1

2 , then 

( )

∏( ) ( ) ∏( )

∑

≠ ≠

≠

− − −×−

−

−=

Ti

i

T

T

Ti

i

Ti

ii

φ

φ

φφ

φφ

χ 11 1

1

2

Define χ ≠Ti the factor χ for all parameters but T. We have:

( )

∏( )

∑

≠

≠

≠ −

−

−=

Ti

i

Ti

ii

Ti

φ

φφ

χ 1

1

2

Then:

( )

( ) ∏( )

≠

≠

− − −

− −=

Ti

i

T

T

Ti

φ

φ

φ

χ χ 1 1

2 2

Then, ξ = χ if 0 < χ < 1; ξ = 1 if χ > 1 and ξ = 0 if χ < 0. 

Note, moreover, that at T = Tref, γT = 1, φT = 0. We can thus define our parameters at T = Tref since 

γTref = γi≠T, χTref = χi≠T. 

In practice

For a given RTE product with a different set of parameters (pH, aw, [LAC], [DAC]) and a μref. Assume 

that only the temperature changes. The following process may be used to evaluate the growth at a 

temperature T. 

For a given RTE product, at T = Tref 

1. Evaluate and store = ∏

i

T i ref γγ . If γTref = 0 ⇒ No growth ∀T
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

152 

2. Evaluate and store ∏( ) −=

i

T i ref

A 1 φ

3. Evaluate and store 

( )

ref

ref

T

i

ii

T A

∑ −

−=

φφ

χ

1

2 . If χTref ≤ 0 ⇒ No growth ∀T

For a given temperature T If T ≤ Tmin ⇒ No growth for T

4. Evaluate 

2 2

min

min

83.27

83.2 



 



 + = 















−

− = T

TT

TT

ref

Tγ

5. Evaluate ( ) T T

T

φ −= γ 













 



 



 + −= 1

83.27

83.2 1

2

6. Evaluate 

ref

ref

T

T

T

T

A

φ

φ

χ

χ − −

− −= 1

2

2 If χ ≤ 0 ⇒ No growth for T

7. If if χ ≥ 1, set ξ = 1; else ξ = χ

8. Evaluate ( ) TTref ×××= ξγγμμ ref
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

153 

Appendix 2: Consumption Data

The objective was to derive a distribution for serving sizes for “Deli meat,” “Deli Cheese,” and “Deli 

Salad.” Consumption data were extracted from the 1999-2006 National Health and Nutrition Examination 

Survey (NHANES, a USDA / DHHS survey) results, using the FARETM Program (Food Analysis and 

Residue Evaluation Program, v. 8.63) developed by Exponent®. Briefly, the nutritional assessment 

component of the NHANES includes a 24-hour dietary recall interview for participants of all ages. 

Dietary recall interviews are conducted, in person, by trained dietary interviewers fluent in Spanish and 

English. Each dietary interview room contains a standard set of measuring guides. These tools are used to 

help the respondent report the volume and dimensions of the food items consumed. They are not intended 

to represent any one particular food, but rather are designed to help respondents estimate portion sizes. 

This set of measuring guides is designed specifically for use in the current NHANES setting with a target 

population of non-institutionalized U.S. civilians. 

Method 

All analyses were performed using the following options:

- Database: NHANES, combined 1999-2006 data, using only the first day of intake data; 

- Uses statistical weights, does not divide intake by body weight;

- Intake timing by specific meal and snack period. For meal and snack period, brunch and lunch 

were combined, as well as supper and dinner. All snacks were gathered daily as a single eating 

occasion;

- The population was the standard U.S. population (all season, region, age, sex, and ethnicity), the 

pregnant 13+ population and the senior (55+) population. 

Deli Meat 

An analysis by ingredient (“RAC”, Raw Agricultural Commodities) was used, because part of this deli 

meat is used within complex food, such as sandwiches. First, the software extracted from the NHANES 

database all recorded eating occasions of any of the foods-as-eaten items listed, considered as including a 

“Deli Meat” as ingredient (Table 20). The list is an update of the 2003 FDA/FSIS risk assessment list 

(provided in [3], appendix 5, p.419). Then, the “Meat” part of these food items was extracted using recipe 

translation files included with the FARETM program. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

154 

Table 20: Food items considered as including “Deli Meat” 

CODE Description

22301000 Ham, fresh, cooked, NS as to fat eaten

22301110 Ham, fresh, cooked, lean and fat eaten

22301120 Ham, fresh, cooked, lean only eaten

22311000 Ham, smoked or cured, cooked, NS as to fat eaten

22311010 Ham, smoked or cured, cooked, lean and fat eaten

22311020 Ham, smoked or cured, cooked, lean only eaten

22311200 Ham, smoked or cured, low sodium, cooked, NS as to fat eaten

22311210 Ham, smoked or cured, low sodium, cooked, lean and fat eaten

22311220 Ham, smoked or cured, low sodium, cooked, lean only eaten

22311450 Ham, prosciutto

23322100 Deer bologna

24201500 Turkey, light or dark meat, smoked, cooked, NS as to skin eaten

24201510 Turkey, light or dark meat, smoked, cooked, skin eaten

24201520 Turkey, light or dark meat, smoked, cooked, skin not eaten

25220010 Cold cut, NFS

25220390 Bologna, beef, lowfat

25220400 Bologna, pork and beef

25220410 Bologna, NFS

25220420 Bologna, Lebanon

25220430 Bologna, beef

25220440 Bologna, turkey

25220450 Bologna ring, smoked

25220460 Bologna, pork

25220470 Bologna, beef, lower sodium

25220480 Bologna, chicken, beef, and pork

25220490 Bologna, with cheese

25220500 Bologna, beef and pork, lowfat

25220710 Chorizos

25221210 Mortadella

25221250 Pepperoni

25221480 Mettwurst

25221500 Salami, NFS

25221510 Salami, soft, cooked

25221520 Salami, dry or hard

25221530 Salami, beef

25221710 Souse

25221810 Thuringer

25230110 Luncheon meat, NFS

25230210 Ham, sliced, prepackaged or deli, luncheon meat

25230220 Ham, sliced, low salt, prepackaged or deli, luncheon meat

25230230 Ham, sliced, extra lean, prepackaged or deli, luncheon meat

25230310 Chicken or turkey loaf, prepackaged or deli, luncheon meat

25230410 Ham loaf, luncheon meat

25230430 Ham and cheese loaf

25230450 Honey loaf

25230510 Ham, luncheon meat, chopped, minced, pressed, spiced, not canned

25230520 Ham, luncheon meat, chopped, minced, pressed, spiced, lowfat, not canned

25230560 Liverwurst

25230610 Luncheon loaf (olive, pickle, or pimiento)

25230710 Sandwich loaf, luncheon meat

25230790 Turkey ham, sliced, extra lean, prepackaged or deli, luncheon meat

25230800 Turkey ham
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

155 

CODE Description

25230810 Veal loaf

25230820 Turkey pastrami

25230840 Turkey salami

25230900 Turkey or chicken breast, prepackaged or deli, luncheon meat

25230905 Turkey or chicken breast, low salt, prepackaged or deli, luncheon meat

25231110 Beef, sliced, prepackaged or deli, luncheon meat

25231150 Corned beef, pressed

27500050 Sandwich, NFS

27500100 Meat sandwich, NFS

27500200 Wrap sandwich, filled with meat, poultry, or fish, vegetables, and cheese

27500300 Wrap sandwich, filled with meat, poultry, or fish, and vegetables

27510910 Corned beef sandwich

27510950 Reuben sandwich (corned beef sandwich with sauerkraut and cheese), with spread

27511010 Pastrami sandwich

27513010 Roast beef sandwich

27513020 Roast beef sandwich, with gravy

27513030 Roast beef sandwich dipped in egg, fried, with gravy and spread

27513040 Roast beef submarine sandwich, with lettuce, tomato and spread

27513050 Roast beef sandwich with cheese

27513060 Roast beef sandwich with bacon and cheese sauce

27513070 Roast beef submarine sandwich, on roll, au jus

27520110 Bacon sandwich, with spread

27520120 Bacon and cheese sandwich, with spread

27520130 Bacon, chicken, and tomato club sandwich, with lettuce and spread

27520135 Bacon, chicken, and tomato club sandwich, with cheese, lettuce and spread

27520140 Bacon and egg sandwich

27520150 Bacon, lettuce, and tomato sandwich with spread

27520160 Bacon, chicken, and tomato club sandwich, on multigrain roll with lettuce and spread

27520165 Bacon, chicken fillet (breaded, fried), and tomato club with lettuce and spread

27520166 Bacon, chicken fillet (breaded, fried), and tomato club sandwich with cheese, lettuce and spread

27520170 Bacon on biscuit

27520250 Ham on biscuit

27520300 Ham sandwich, with spread

27520310 Ham sandwich with lettuce and spread

27520320 Ham and cheese sandwich, with lettuce and spread

27520330 Ham and egg sandwich

27520350 Ham and cheese sandwich, with spread, grilled

27520360 Ham and cheese sandwich, on bun, with lettuce and spread

27520370 Hot ham and cheese sandwich, on bun

27520380 Ham and cheese on English muffin

27520390 Ham and cheese submarine sandwich, with lettuce, tomato and spread

27520410 Cuban sandwich, (Sandwich cubano), with spread

27520540 Ham and tomato club sandwich, with lettuce and spread

27540110 Chicken sandwich, with spread

27540130 Chicken barbecue sandwich

27540290 Chicken submarine sandwich, with lettuce, tomato, and spread

27540310 Turkey sandwich, with spread

27540330 Turkey sandwich, with gravy

27540350 Turkey submarine sandwich, with cheese, lettuce, tomato and spread

27541000 Turkey, ham, and roast beef club sandwich, with lettuce, tomato and spread

27560000 Luncheon meat sandwich, NFS, with spread

27560110 Bologna sandwich, with spread

27560120 Bologna and cheese sandwich, with spread

27560510 Salami sandwich, with spread
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

156 

CODE Description

27560910 Cold cut submarine sandwich, with cheese, lettuce, tomato, and spread

Deli Cheese 

For deli cheese, an analysis using the same option of the FARETM program was performed on the food 

items shown in Table 21. The analysis was based on Dun & Bradstreet (DNB) ingredients (USDA 

Nutrient Databank Identifier). 

Table 21: Food items considered as including “Deli Cheese” 

NDB Code Description

1004 Cheese, blue

1005 Cheese, brick

1009 Cheese, cheddar

1011 Cheese, Colby

1018 Cheese, Edam

1020 Cheese, Fontina

1022 Cheese, Gouda

1023 Cheese, Gruyère

1024 Cheese, Limburger

1025 Cheese, Monterey

1030 Cheese, Muenster

1035 Cheese, provolone

1040 Cheese, Swiss

1042 Cheese, pasteurized process, American, with di sodium phosphate

1043 Cheese, pasteurized process, pimento

1044 Cheese, pasteurized process, Swiss, with di sodium phosphate

1046 Cheese food, pasteurized process, American, without di sodium phosphate

Deli Salad 

The list of foods-as-eaten used to identify deli salad is an update (Table 22) of the one provided for the 

2003 FDA/FSIS risk assessment (see [3] appendix 5, p. 429). 

Table 22: Food items considered as “Deli Salad”.

Code Description

25240000 Meat spread or potted meat, NFS

25240110 Chicken salad spread

25240220 Ham salad spread

25240310 Roast beef spread

25240320 Corned beef spread

27416250 Beef salad

27420020 Ham or pork salad

27446200 Chicken or turkey salad

27446205 Chicken or turkey salad with nuts and/or fruits
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

157 

Code Description

27446220 Chicken or turkey salad with egg

27446300 Chicken or turkey garden salad (chicken and/or turkey, tomato and/or carrots, other 

vegetables), no dressing

27446310 Chicken or turkey garden salad (chicken and/or turkey, other vegetables excluding tomato 

and carrots), no dressing

27446315 Chicken or turkey garden salad with bacon (chicken and/or turkey, bacon, cheese, lettuce 

and/or greens, tomato and/or carrots, other vegetables), no dressing

27446320 Chicken or turkey (breaded, fried) garden salad with bacon (chicken and/or turkey, bacon, 

cheese, lettuce and/or greens, tomato and/or carrots, other vegetables), no dressing

27446350 Oriental chicken or turkey garden salad (chicken and/or turkey, lettuce, fruit, nuts), no 

dressing

27446355 Oriental chicken or turkey garden salad with crispy noodles (chicken and/or turkey, lettuce, 

fruit, nuts, crispy noodles), no dressing

27446360 Chicken or turkey caesar garden salad (chicken and/or turkey, lettuce, tomato, cheese), no 

dressing

27446362 Chicken or turkey (breaded, fried) caesar garden salad (chicken and/or turkey, lettuce, 

tomatoes, cheese), no dressing

27450010 Crab salad

27450020 Lobster salad

27450030 Salmon salad

27450060 Tuna salad

27450070 Shrimp salad

27450080 Seafood salad

27450090 Tuna salad with cheese

27450100 Tuna salad with egg

27450110 Shrimp garden salad (shrimp, lettuce, eggs, tomato and/or carrots, other vegetables), no 

dressing

27450120 Shrimp garden salad (shrimp, lettuce, eggs, vegetables excluding tomato and carrots), no 

dressing

27450130 Crab salad made with imitation crab

27450180 Seafood garden salad with seafood, lettuce, vegetables excluding tomato and carrots, no 

dressing

27450190 Seafood garden salad with seafood, lettuce, tomato and/or carrots, other vegetables, no 

dressing

27450200 Seafood garden salad with seafood, lettuce, eggs, vegetables excluding tomato and carrots, no 

dressing

27450210 Seafood garden salad with seafood, lettuce, eggs, tomato and/or carrots, other vegetables, no 

dressing

27460490 Julienne salad (meat, cheese, eggs, vegetables), no dressing

27460510 Antipasto with ham, fish, cheese, vegetables

27520340 Ham salad sandwich

27540120 Chicken salad or chicken spread sandwich

27540320 Turkey salad or turkey spread sandwich

27550710 Tuna salad sandwich, with lettuce

27550720 Tuna salad sandwich

27550750 Tuna salad submarine sandwich, with lettuce and tomato

32103000 Egg salad

32203010 Egg salad sandwich

41203020 Kidney bean salad

41205070 Hummus

58101930 Taco or tostada salad with beef, beans and cheese, fried flour tortilla

58101940 Taco or tostada salad, meatless, with cheese, fried flour tortilla

58148110 Macaroni or pasta salad

58148120 Macaroni or pasta salad with egg
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

158 

Code Description

58148130 Macaroni or pasta salad with tuna

58148140 Macaroni or pasta salad with crab meat

58148150 Macaroni or pasta salad with shrimp

58148160 Macaroni or pasta salad with tuna and egg

58148170 Macaroni or pasta salad with chicken

58148180 Macaroni or pasta salad with cheese

58148500 Pasta or macaroni salad with oil and vinegar-type dressing

58148550 Pasta or macaroni salad with meat

71601010 Potato salad with egg

71602010 Potato salad, German style

71603010 Potato salad

72116140 Caesar salad (with romaine)

73101010 Carrots, raw

73101110 Carrots, raw, salad

73101210 Carrots, raw, salad with apples

74506000 Tomato and cucumber salad made with tomato, cucumber, oil, and vinegar

75140500 Broccoli salad with cauliflower, cheese, bacon bits, and dressing

75141000 Cabbage salad or coleslaw, with dressing

75141100 Cabbage salad or coleslaw with apples and/or raisins, with dressing

75141200 Cabbage salad or coleslaw with pineapple, with dressing

75142500 Cucumber salad with creamy dressing

75142550 Cucumber salad made with cucumber, oil, and vinegar

75142600 Cucumber salad made with cucumber and vinegar

75143000 Lettuce, salad with assorted vegetables including tomatoes and/or carrots, no dressing

75143050 Lettuce, salad with assorted vegetables excluding tomatoes and carrots, no dressing

75143100 Lettuce, salad with avocado, tomato, and/or carrots, with or without other vegetables, no 

dressing

75143200 Lettuce, salad with cheese, tomato and/or carrots, with or without other vegetables, no 

dressing

75143300 Lettuce, salad with egg, tomato, and/or carrots, with or without other vegetables, no dressing

75143350 Lettuce salad with egg, cheese, tomato, and/or carrots, with or without other vegetables, no 

dressing

75144100 Lettuce, wilted, with bacon dressing

75145000 Seven-layer salad (lettuce salad made with a combination of onion, celery, green pepper, 

peas, mayonnaise, cheese, eggs, and/or bacon)

75146000 Greek Salad

75147000 Spinach salad, no dressing

75148000 Cobb salad with dressing

75201030 Artichoke salad in oil

75302080 Bean salad, yellow and/or green string beans

75416500 Pea salad

75416600 Pea salad with cheese

Results

The FARETM program provides bins of grams per eating occasion and corresponding weighted 

occurrences for breakfast, lunch, dinner, and snack as well as a Total. As an example, 537,349 (weighted) 

eating occasions of (0.35-0.70)g of deli cheese are recorded in the 1999-2006 NHANES data base. 
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

159 

g Occurrences per defined eating occasion (1000):

per eating occ SNACKS Total

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

0 to 0.3 123313 222637

0.3502069 to 0.7004138 211539 537349

0.700414 to 1.050621 214141 787902

1.050621 to 1.400828 163497 763834

1.400828 to 1.751034 185311 744600

1.751034 to 2.101241 100822 908607

2.101241 to 2.451448 337827 1246231

2.451448 to 2.801655 34015 643428

2.801655 to 3.151862 136648 579838

3.151862 to 3.502069 72437 536624

...

The cumulative distribution of the serving size per eating occasion for deli meat, deli cheese, and deli 

salad for the total, the pregnant, and the senior populations are provided in Figure 64, Figure 65, and 

Figure 66. 

Figure 64: Empirical cumulative density function of the serving size per eating occasion (unit: g/EO) for 

deli-meat for the total population, pregnant women, and seniors (55+): data NHANES 1999-2006.
Interagency Risk Assessment: L. monocytogenes in Retail Delicatessens – Technical Report

160 

Figure 65: Empirical cumulative density function of the serving size per eating occasion (unit: g/EO) for 

deli-cheese for the total population, pregnant women, and seniors (55+): data NHANES 1999-2006

Figure 66: Empirical cumulative density function of the serving size per eating occasion (unit: g/EO) for 

deli-salad for the total population, pregnant women, and seniors (55+): data NHANES 1999-2006

Simulation 

To provide simulated serving sizes within this “interagency L. monocytogenes in retail risk assessment” 

model, we used the following algorithm: 

- from the FARE results, one bin is randomly sampled proportionally to its number of occurrence; 

- then, a serving size is randomly sampled uniformly within the bounds of this bin; 

- the value is rounded to the upper gram. 

The resulting empirical cumulative distributions and their relative statistics for the overall population are 

provided in Figure 11 of this document.