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DRAFT

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## MICROBIA L 11



## RISK ASSESSMENT GUIDELINE 12

13



## PATHOGENIC MICROORGANISMS 14



## WITH FOCUS ON FOOD AND WATER 15

16

17



## Prepared by the Interagency Microbiological 18



## Risk Assessment Guideline Workgroup 19

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# DRAFT 24



## Version 5. 3 25

26

June 20 , 2011 27

28Page ii Microbia l Risk Assessment Guideline



## DISCLAIMER 1

2

This guideline document represents the current thinking of the w orkgroup on the 3

topics addressed. It is not a regulation and does not confer any rights for or on any person 4

and does not operate to bind USDA, EPA, any other federal agency, or the public. It is 5

within the discretion of each federal agency to determine whether or how to utilize this 6

guideline. 7

8

This draft guideline is distributed solely for the purpose of pre

- dissemination peer 9

and public review under applicable information qu ality guidelines. It has not been formally 10

disseminated by USDA or EPA. It does not represent and should not be construed to 11

represent any agency determination or policy. 12

13

Mention of trade names or commercial products does not constitute endorsement 14

or r ecommendation for use. 15

16

17Microbia l Risk Assessment Guideline Page iii

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## TABLE OF CONTENTS 1

Disclaimer

................................ ................................ ................................ .......................... ii 2

Interagency Workgroup Members

................................ ................................ ................ vii 3

Preface

................................ ................................ ................................ ............................. viii 4

1. Introduction

................................ ................................ ................................ .................. 1 5

1.1 What is Human Health Risk Assessment?

................................ ....................... 1 6

1.2 What Led to the Decision to Create this MRA Guideline?

............................ 2 7

1.3 What are the Benefits of Developing a Common MRA Guideline?

.............. 4 8

1.4 When Can I Apply this MRA Guideline?

................................ ........................ 4 9

1.5 What are the Fundamental Differences between Microbes and Chemicals? 5 10

1.6 What is the Relationship of Infectious Disease as Applied to Human Health 11

in a MRA?

................................ ................................ ................................ ........... 8 12

1.7 What are the Benefits of Iterative MRA?

................................ ........................ 9 13

1.8 How Does This Guideline Fit in with My Agency’s Current MRA 14

Approaches and Practices?

................................ ................................ ............... 9 15

1.9 How is this MRA Guidance Related to Other MRA 16

Frameworks/Guidelines that a re Currently Available?

............................... 1 0 17

1.10 What are Major Principles for MRA?

................................ ........................... 11 18

2. Planning and Scoping

................................ ................................ ................................ 15 19

2.1 What is Planning and Scoping?

................................ ................................ ...... 15 20

2.1.1 What is Problem Formulation?

................................ ............................ 16 21

2.2 What are the Be nefits of Planning and Scoping?

................................ .......... 16 22

2.3 What do I Consider When Deciding to Initiate a MRA?

............................. 17 23

2.4 Who Can be Involved with Plann ing and Scoping?

................................ ..... 19 24

2.5 How Can the MRA be Used?

................................ ................................ .......... 20 25

2.5.1 What “Depth” Can I go into in the Risk Assessment?

....................... 21 26

2.5.2 What Are Examples of Types of MRA?

................................ .............. 22 27

2.5.3 What are Threat and Vulnerability Assessments for High

28

Consequence Biological Events?

................................ .......................... 23 29

2.6 What is Discussed During Planning and Scoping and What Products 30

Emerge?

................................ ................................ ................................ ............ 25 31

2.6.1 What are Risk Management Questions and What is t he Charge?

... 29 32

2.6.2 What is a Risk Profile?

................................ ................................ ......... 30 33

2.6.3 What is a Conceptual Model?

................................ .............................. 30 34

2.6.4 What is an Analysis Plan?

................................ ................................ .... 31 35

2.6.5 How are Data Gaps Identified and Addressed in the Context of 36

Planning and Scoping?

................................ ................................ .......... 31 37

2.6.6 How do I Consider Information Quality Including Data Quality?

.. 35 38

2.6.7 What is a Value

- of

- Information Analysis?

................................ ......... 38 39

2.6.8 What is a Communications Plan?

................................ ........................ 39 40

2.7 What Types of Decisions within Risk Assessment are Science Policy?

....... 39 41

2 .8 What is My Role as a Risk Assessor in Risk Assessment?

........................... 41 42

3. Hazard Identification and Hazard Characterization

................................ ............. 43 43

3.1 How do I Define the Hazard?

................................ ................................ ......... 43 44

3.2 What are Hazard Identification and Hazard Characterization?

................ 44 45

3.3 What Hazard Characteristics Can I Consider?

................................ ............ 45 46Page iv Microbia l Risk Assessment Guideline

3.4 How do Microbial Hazards Cause Adverse Outcomes?

.............................. 46 1

3.4.1 What does Virulence and/or Pathogenicity Mean in th e Context of 2

Causing an Adverse Outcome?

................................ ............................ 47 3

3.5 What are the Mechanisms that May Lead to the Development of New 4

Pathogens or Pathogens with New Traits?

................................ .................... 47 5

3.6 What are the Major Categories of Microorganisms?

................................ ... 49 6

3.7 What Methodological Approaches are Used to Identify and Quantify 7

Microorganisms?

................................ ................................ .............................. 52 8

3.8 Are there any Special Concerns Regarding Microbial Detection Methods? 9

................................ ................................ ................................ ............................ 53 10

3.9 What Host Factors Can I Take into Consideration?

................................ .... 56 11

3.10 How does Life Stage Affect Sensitivity to Infection and Disease 12

Manifestation?

................................ ................................ ................................ .. 58 13

3.11 What Environmental Factors Can I Take into Considera tion?

.................. 59 14

4. Dose

- Response Assessment

................................ ................................ ....................... 61 15

4.1 What is Dose

- Response Modeling and What are Some General 16

Considerations for Dose

- Re sponse Modeling?

................................ .............. 61 17

4.1.1 How do I Choose Between Modeling a Discrete Dose Versus an 18

Average Dose?

................................ ................................ ........................ 62 19

4.1.2 What is the One

- Hit Model and Why is it the Preferred Model?

..... 63 20

4.1.3 What Important Factors Can I Consider in Dose

- Response 21

Assessment ?

................................ ................................ ........................... 65 22

4.1.4 Ho w Can I Model the Spread of Disease in the Population?

............. 70 23

4.1.5 What Can I Address for Each Model to Improve Transparency?

... 71 24

4. 2 What is Current Practice in Quantitative Dose

- Response Modeling for 25

Microbial Illness?

................................ ................................ ............................. 72 26

4.2.1 What Models Can I Use for Microbial Dose

- Response Assessment? 72 27

4.2.2 What is the Output of a Dose

- Response Assessment?

........................ 79 28

4.2.3 How do I Fit Models to Existing Dose

- Response Data?

..................... 80 29

4.2.4 How Can I Evaluate Uncertainty in Dose

- Response?

........................ 82 30

4.2.5 What is Variability in Dose

- Response?

................................ ............... 82 31

4.2.6 How Can I Account for Life Stages and Subpopulations in Dose

32

Response Models?

................................ ................................ .................. 83 33

4.2.7 Can I Use Uncertainty, Modifying, or Adjustment Factors in a 34

Microbial Dose

- Response Assessment ?

................................ ............... 84 35

4.2.8 What Modeling Methods are on the Horizon?

................................ ... 85 36

5. Exposure Assessment

................................ ................................ ................................ . 86 37

5.1 What are General Concepts in Exposure Assessment?

................................ 86 38

5.1.1 What is an Exposure Assessment?

................................ ....................... 86 39

5.1.2 What is Exposure?

................................ ................................ ................. 87 40

5.1.3 What are Sources, Pathways, and Routes of Exposure?

................... 87 41

5.1.4 What Environmental Factors Can I Take into Co nsideration?

........ 90 42

5.1.5 What is an Exposure Scenario?

................................ ........................... 91 43

5.1.6 What are Qualitative and Quantitative Exposure Assessments?

..... 91 44

5.1.7 What is Variability in Exposure Assessment?

................................ .... 92 45

5.1.8 What is Uncertainty in Exposure Assessment?

................................ .. 92 46Microbia l Risk Assessment Guideline Page v

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................................ . 93 1

5.1.10 What is a Stochastic Exposure Assessment?

................................ ....... 94 2

5.1.11 What is Monte Carlo Analysis?

................................ ........................... 94 3

5.1.12 How does Exposure Assessment Fit with the Other Components of 4

Risk Assessment?

................................ ................................ ................... 96 5

5.1.13 Do Different Exposure Scenarios Always Generate Different 6

Microbial Doses?

................................ ................................ ................... 97 7

5.2 How do I Develop an Exposure Assessment?

................................ ................ 99 8

5.2.1 What is the Purpose of the Risk Assessment?

................................ .... 99 9

5.2.2 Which Scenarios Can I Consider?

................................ ..................... 100 10

5.2.3 What are the Exposed Populations I Could Consider?

................... 105 11

5.2.4 What are Common Approaches to Exposure Modeling I Can Use? 12

................................ ................................ ................................ ............... 106 13

5.2.5 How is Scenario Analysis Used in Exposure Assessment?

.............. 109 14

5.2.6 What is the Role of Predictive Microbiology in Exposure 15

Assessment?

................................ ................................ ......................... 114 16

5.2.7 How Can I Address Secondary Transmission of Disease in the 17

Population?

................................ ................................ .......................... 117 18

5.2.8 What Data Can I Use in an Exposure Assessment?

......................... 118 19

5.2.9 How do I Use Data in an Exposure Assessment?

............................. 120 20

5.3 How do I Analyze a Model’s Results?

................................ .......................... 121 21

5.3.1 How do I Report Exposure in an Exposure Assessment?

................ 122 22

5.3.2 How do I Determine a Change in Exposure and Subsequent Risk? 123 23

5.3.3 Wha t is Sensitivity Analysis?

................................ .............................. 124 24

5.3.4 What is an Uncertainty Analysis?

................................ ...................... 125 25

5.4 What Can I Put Into an Exposure Assessment Report?

............................ 127 26

5.5 What are Possible Future Developments in Exposure Assessment?

......... 128 27

6. Risk Characterization

................................ ................................ .............................. 130 28

6.1 What is Risk Characterization?

................................ ................................ ... 130 29

6.2 What are the Elements in a Risk Characterization?

................................ .. 132 30

6.3 How Do I Prepare a Risk Characterization?

................................ .............. 135 31

6.4 Are All Risk Characterizations Quantitative and What Do I Do When 32

Quantitative Data are Unavailable for Some Elements of the Ris k 33

Characterization?

................................ ................................ .......................... 137 34

6.5 Are There Different Forms of Risk Characterization? When Do I Apply 35

Them?

................................ ................................ ................................ .............. 137 36

6.5.1 When is a Static Model Appropriate?

................................ ................. 139 37

6.5.2 When is a Dynamic Model Appropriate?

................................ ........... 140 38

6.6 How are Sensitivity and Uncertainty Analyses Related to Risk 39

Characterization?

................................ ................................ .......................... 142 40

6.7 How are Quality of Life Measures Important in MRA?

............................ 144 41

6.8 How Can a Risk Assessment be Valid ated?

................................ ................ 146 42

7. Risk Management

................................ ................................ ................................ .... 148 43

7.1 What is Risk Management?

................................ ................................ .......... 148 44

7.2 When and How Can Risk Managers be Involved in Risk Assessments?

.. 150 45Page vi Microbia l Risk Assessment Guideline

7.3 How are Risk Management Options a Useful Component to Include in a 1

Risk Assessment?

................................ ................................ ........................... 152 2

7.4 What are Some Other Inputs into Risk Management Decisions About 3

Controlling or Accepting Risks?

................................ ................................ ... 152 4

7.5 What are Some Operational Risk Management Too ls and Approaches? 155 5

7.6 What is Risk Management for the Intentional use of Regulated 6

Microorganisms?

................................ ................................ ............................ 156 7

8. Risk Communicati on

................................ ................................ ............................... 157 8

8.1 What is Risk Communication?

................................ ................................ ..... 157 9

8.2 What are the Benefits of Risk Communication?

................................ ......... 157 10

8.3 Who are the Stakeholders of MRAs?

................................ ........................... 158 11

8.4 With Whom Can I Communicate?

................................ ............................... 159 12

8.5 When Can the Process of Risk Communication Begin?

............................ 159 13

8.6 Can I Communicate in Writing, Orally, or Both?

................................ ...... 15 9 14

8.7 Who Decides What to Communi cate?

................................ ......................... 160 15

8.8 What Information Can be Communicated?

................................ ................ 160 16

8.9 How is the Communication Process a Continuous Dialog?

....................... 161 17

8.10 How In

- Depth Can I Communicate?

................................ ........................... 162 18

8.11 What Can I Do if the Message is not “Getting Through?”

........................ 162 19

8.12 How Can I Communicate Risk Successfully?

................................ ............. 163 20

8.13 How Can I Handle Media and Congressional Office Requests?

............... 164 21

8.14 When Can Risk Communication End?

................................ ........................ 164 22

9. Glossary

................................ ................................ ................................ .................... 166 23

10. Abbreviations

................................ ................................ ................................ ......... 175 24

11. References

................................ ................................ ................................ ............... 178 25

Appendix A Example Assumptions

................................ ................................ ................. 1 26

Appendix B Hazard Identification Ques tions

................................ ................................ 1 27

28

29

30Microbia l Risk Assessment Guideline Page vii

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## INTERAGENCY WORKGROUP MEMBERS 1

2

Kerry Dearfield, Co

- Chair USDA/FSIS 3

Steve Schaub, Co

- Chair EPA/OW (retired) 4

Nicholas Ashbolt, Co

- Chair EPA/ORD 5

Irwin Baumel EPA/ OR D 6

Michael Broder EPA/ORD 7

Uday Dessai USDA/FSIS 8

Moshe Dreyfuss USDA/FSIS 9

Eric Ebel USDA/FSIS 10

Brendlyn Faison EPA/OW 11

Julie Fitzpatrick EPA/ORD 12

Joel Gagliardi EPA/OPP 13

Frank Hearl CDC/NIOSH 14

Abdel Kadry EPA/ORD 15

Janell Kaus e USDA/FSIS 16

Barbara Klieforth EPA/OSA 17

Ken Martinez CDC/NIOSH 18

Robert McDowell USDA/APHIS 19

Stephen Morse CDC /NCEZID 20

Tonya Nichols EPA/ORD 21

Mark Ott NASA 22

Duane Pierson NASA 23

William Schneider EPA/OPP 24

Carl Schroeder USDA/FSIS 25

Mark Segal EPA/OPPT 26

Sean Shadomy CDC /NCEZID 27

Jeff Swartout EPA/ORD 28

Sara h Taft EPA/ORD 29

Brandolyn Thran AIPH 30

Elizabeth (Betsy) Weirich CDC /NCEZID 31

32

Other contributors (not currently on workgroup): Bonnie Gaborek (USACHPPM); Myra 33

Gardner (USDA/FSIS); Alecia Naugle (USDA/FS IS); Geoff Patton (EPA/ORD) ; Gary 34

Bangs (EPA/RAF); Deborah McKean (EPA/ ORD ); Gregory Stewart (State) ; Parmesh 35

Saini (USDA/FSIS) ; Gregg Claycamp ( FDA/CDER ) 36

37

Contractor support: Audrey Ichida (ICF International), Jeff Soller (Soller 38

Environmental), Margaret Coleman (ICF International) , and Heather Simpson (ICF 39

International). 40

41

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44Page viii Microbia l Risk Assessment Guideline



## PREFACE 1

2

This Microbial Risk Assessment (MRA) Guideline is written to be a resource for 3

microbial risk assessors at the U.S. Department of Agriculture‘s Food Safety and 4

Inspection S ervice and the U.S. Environmental Protection Agency and their agents, 5

contractors, and the general risk assessment community with which these agencies 6

interact. Other Federal agencies expressed interest in the development of this guideline 7

and provided ex perts to participate in this interagency effort (see list of participants). 8

9

In recognition of the needs and mandates of the participating Agencies and the 10

various statutory authorities that may apply to microbial risk assessment (MRA), this 11

Guideline em phasizes the need for a flexible template for conducting microbial risk 12

assessment. It provides general, broad fundamental risk assessment principles 13

specifical ly for microbial risks, but is not intend ed to be prescriptive nor is it intended to 14

supplant t he internal practices or policies of any Federal agency

. Users have the 15

flexibility to adapt pertinent sections to relevant statutory authorities and purposes if 16

needed. On the individual level, it is written for persons with some knowledge of 17

microbiolo gy and basic understanding of risk assessment principles but some basics may 18

be presented at the introduction to a topic

. T he intention is that this Guideline can be 19

periodically updated, particularly as more information becomes available. 20

21

This G uideli ne primarily focuses on infectious diseases associated with the 22

g astrointestinal tract and fecal or oral transmission of the causal agents mainly in food 23

and water , but clearly has application to other scenarios

. 24

25

Many human pathogens found in food, water , and the environment cause acute 26

diseases that have short incubation periods, symptoms typically lasting several days to a 27

week, and usually non

- lethal effects with complete recovery from the illness . However, 28

some pathogens associated with the gastroint estinal tract may cause more serious 29

diseases or sequelae such as reactive arthritis, ca ncer , Guillain

- Barr é syndrome, and 30

juvenile onset diabetes which may have long

- term implications. Further, there are 31

indigenous water

- based pathogens, such as Legionel la spp. and Mycobacterium spp.

, that 32

grow in biofilm environments and their inhalation via aerosols may cause pneumonia, 33

which can be fatal. Applying risk assessment approaches associated with MRA 34

procedures discussed in this guideline should help risk as sessors characterize the 35

common exposure sources, causative agents, associated symptoms, contributing 36

immunity factors, and other common threads contributing to chronic illness. 37

38

The Guideline does not specifically include biological warfare agents, airbor ne 39

microbial hazards, or agriculturally or industrially important microorganisms

, 40

oligonucleotides, prions, preformed microbial toxins, and other submicrobial entities. 41

These agents have many unknowns associated with their sources, modes of ―infection‖ 42

an d disease, transmissibility, and survivability. Nonetheless, information in this 43

guideline may provide information to risk assessors addressing these issues

. 44

45Microbia l Risk Assessment Guideline Page 1

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## 1. INTRODUCTION 1

2

3

Microbial risk assessments (MRA) are a valuab le tool for Federal A ge ncies 4

tasked with understanding , reducing , and preventing risks presented by hazardous 5

microorganisms, whether natural or anthropogenic, intentional or unintended. Increasing 6

globalization has compounded these risks, with th e broadening and often rapid 7

distribution of illnesses

. Clear and credible ri sk assessment methods are proving ever 8

more necessary for agencies to address both current and future risks associated with 9

contamination of air, water, soil and food by bacteria

, fungi, protozoa, viruses, and their 10

toxins

. 11

12

This guideline is intended to layout an overarching approach to conducting 13

microbial risk assessment and to introduce the users to tools and methods available to 14

them. Additionally, it is expected to promote consistency and improve transparency in 15

the way

- microbial risk assessment s are conducted . Nonetheless this Guideline is not 16

intended to replace existing guidelines that are in use by Agencies. The decision to apply 17

methods and approaches in this guideli ne, either totally or in part, is left to the discretion 18

of the individual department or agency. This document is offered to provide information 19

that may be useful for microbial risk assessors. 20

21

22

1.1 What is Human Health Risk Assessment? 23

24

Risk assessment is widely recognized as a systematic way to prepare

, organize

, 25

and analyze information to help make regulatory decisions, establish programs, and 26

prioritize research and development efforts . In Risk Assessment in the Federal 27

Government; Managing the Process

, published by the National Research Council of the 28

National Academies of Science (NRC, 1983; hereafter referred to as the ― NRC 1983 29

report‖ ), risk assessment is defined as ― the qualitative or quantitative characterization of 30

the potential health effects of particular substances on individuals or populations

, ‖ 31

structured to include a hazard identification ( HI ) , dose

- response assessment, exposure 32

assessment, and risk characterization. Similarly , the Codex Alimentarius Commission, 33

established by the United Nations Food and Agriculture Organization (FAO) and World 34

Health Organization (WHO) and recognized by the World Trade Organization as the 35

relevant organization for international food safety sta ndards and guidelines

, defined risk 36

assessment as ― a scientifically based process consisting of the following steps: (i) hazard 37

identification, (ii) hazard characterization, (iii) exposure assessment, and ( iv ) risk 38

characterization‖ in Principles and Guide lines for the Conduct of Microbiological Risk 39

Assessment (C odex, 1999 ; hereafter referred to as the Codex framework ). 40

41

The risk assessment process is used to facilitate the application of science to 42

policy decisions. R isk assessment at the Federal level in form s the risk management 43

decision making process and risk communication through organized scientific analyses of 44

data related to a specified hazard. Risk assessment can also evaluate potential or 45

proposed risk management strategies‘ impact on public heal th. Essentially, a MRA is the 46Page 2 Microbia l Risk Assessment Guideline

formal, scientifically based process to estimate the likelihood (probability) of exposure to 1

a microbial hazard and the resulting public health ( and/or environmental ) impact from 2

this exposure. Risk assessment not only inclu des the likelihood of exposure and the 3

impact of that exposure, but also includes steps for planning

, scoping

, and hazard 4

identification and c haracterization (see Section 1.4 ). This G uideline will only focus on 5

MRA conducted for public health purposes

. 6

7

Risk assessment

, risk management

, and risk communication are the three 8

components of a risk analysis. Figure 1.1 is a representation of how these terms are 9

related. The three activities are shown as partially overlapping because they depend up on 10

each oth er and draw from each other. This Guid eline addresses risk management and risk 11

communication from the perspective of what a risk assessor should know about those 12

disciplines and provides transparency to decision makers and stakeholders regarding how 13

a MRA is conducted. Therefore

, the risk communication and risk management chapters 14

in this Guideline provide only a small portion of what those disciplines contribute to risk 15

analysis as a whole. 16

17

Figure 1.1 Risk Assessment, Management, and Communication in Ri sk Analysis 18

19

1.2 Wh at Led to the Decision to Create this MRA Guideline ? 20

21

Government Agencies have conducted f ormal risk assessments for chemicals in 22

food, water, and the natura l environment for decades. These assessments had their 23

origins in support of or in response to the establishment of a number of laws and 24

regulations that required the federal government to control chemical contaminants in food 25

and environmental media. Th e NRC 1983 report helped unify the risk assessment 26

processes for chemicals in foods and the environment and provided a framework that 27

federal agencies, their clients, and the risk assessment community in general could apply 28

in conducting risk assessments. Since then, the NRC 1983 report has been used by 29

regulatory agencies as guidance in conducting risk assessments. 30Microbia l Risk Assessment Guideline Page 3

Draft

– Do not cite, circulate, or quote 1

The need for a common chemical risk assessment approach was established in the 2

1980s, before there was a similar requirement for a MRA appr oach. At that time, MRA 3

limitations included lack of data, tools, and methods, such as limited dose

- response 4

models, poor quantification of microbial occurrence, limited analytical methods 5

(sensitivity, specificity, precision, and accuracy), and poor unde rstanding of human 6

immunological responses. Since the 199 0s , the use of MRA s has gained significant 7

credibility in the federal regulatory community, as new information on the identification 8

of infectious microbial pathogens, their occurrence, potential fo r human exposure, dose

9

response, and attributable health effects became increasingly available. A number of 10

credible mathematical models, protocols, and other tools have become available that 11

allow MRAs to be conducted even with substantial variability, u ncertainty, and lack of 12

specific data

– issues that previously would have impeded preparation of quantitative risk 13

assessments. Indeed methods are now available to specifically capture and report 14

variability and uncertainty associated with data use in MRA

. During the 1990s , it also 15

became apparent that the NRC 1983 report ha d some shortcomings for conducting MRAs 16

since chemicals are different from microorganisms in a number of ways (see Section 1.5 ). 17

The NRC 1983 report provides a very useful foundation f or chemical and microbial risk 18

assessment, but does not provide the level of detail that is required to conduct a 19

quantitative microbial risk assessment. Chemical risk assessors developed more detailed 20

guidelines to expand on the NRC 1983 report. The det ailed chemical risk assessment 21

protocols clearly would not work for microorganisms, so it became necessary for MRA to 22

consider microbial aspects that are not considered in the NRC 1983 report. While 23

Agencies conducting MRAs had for a time continued to gene rally rely on the NRC 1983 24

report, they have individually made adjustments to adapt it for MRA. For example, the 25

FAO of the United Nations and the WHO use the international Codex framework, which 26

follows the same overall structure as the NRC 1983 report, to conduct a MRA of Listeria 27

monocytogenes in ready

- to

- eat foods (FAO/WHO, 2004) an d the Environmental 28

Protection Agency ( EPA ) used the framework in the NRC 1983 report to evaluate the 29

public health impact of drinking water regulations for Cryptosporidium oocysts (EPA, 30

2006a). 31

32

The workgroup that created this Guideline did so with the intent of leveraging 33

limited resources, improving efficiencies, promoting joint interaction, exploiting their 34

individual experiences, and improving consistency in the way the microbial risk 35

assessments are done and thereby fostering greater transparency. This Guideline does not 36

contain every tool that can be used in MRA, but it does include the most widely used 37

tools. This Guideline also is considerably longer and more detai led than the MRA 38

frameworks that precede it (Codex, 1999; ILSI, 2000; Codex, 2007a, 2007b). EPA 39

Office of Water‘s Protocol for Microbial Risk Assessment and the EPA Science Advisory 40

Board‘s review of that document were also considered during the developme nt of this 41

document (EPA, 2009a). 42

43

44Page 4 Microbia l Risk Assessment Guideline

1.3 What are the Benefits of Developing a Common MRA Guideline? 1

2

Agencies that need to be concerned about pathogens often have similar 3

requirements to protect the health of potentially exposed people. For example, a number 4

of pathogens of concern originate in the gastrointestinal tract of humans and a nimals and 5

can potentially contaminate food, surface water, or drinking water

. The agen cies that 6

regulate food and environmental contaminants recognize that the ultimate sources of 7

pathogens are the same for different media. Because the health effects and dose

- response 8

relationship s are similar among different matrices for many of the patho gens, it is useful 9

to have common principles and approaches to assess risks across media and exposure 10

settings. 11

12

This Guideline facilitates s ystematic and transparent consider ation of all relevant 13

factors that impact the risk assessment and facilitates rep roducible risk evaluation

. 14

Agencies assessing a similar medi um or pathogen can more readily compare and contrast 15

the details and assumptions of their assessment to another Agencies‘ assessment. This is 16

not to say that each risk assessment will be complet ely cross

- comparable with other risk 17

assessments , since there are a number of specific data sources and Agency requirements 18

that require different inp u ts and applications. The difference in requirements is why this 19

G uideline is designed to be modular and able to provide flexibility for each Agency‘s 20

specific requirements. On an international scale, there is also a need to have common 21

approaches to MRA to effectively satisfy international trade agreements and public health 22

protection for importation of foo d and beverage products and assess international risks 23

from emerging pathogens around the world. 24

25

This G uideline is the result of the collaborations of microbial risk assessors from 26

a number of Federal Agencie s (USDA/FSIS, USDA/APHIS, EPA/OW, EPA/ORD, 27

EP A/OPP, EPA/OPPT, EPA/OSA, FDA/CDER, CDC/NIOSH, CDC/NCEZID, NASA

, 28

USACHPPM) who have dedicated their time and expertise to prepare a general guideline 29

that all microbial risk assessors in the Government, their contractors, and the risk 30

assessment community in general can apply for conducting MRAs of infectious agents or 31

disease

- causing microorganisms

. The workgroup that developed this Guideline agreed 32

that this document must be general enough so that any Agency , that chooses to do so, can 33

use it effectively

. By being based on general and modular guidelines, e ach specific risk 34

assessment component can then be supplemented (or modified) to suit the particular 35

legislative, legal, or exposure factor needs of an agency. 36

37

The Office of Management and Budget ‘s ( OMB) Good Guidance Practices has 38

been followed as this Guideline was developed (OMB, 2007a). 39

40

1.4 When Can I Apply this MRA Guideline? 41

42

This g uideline is applicable to a wide array of scenarios b ut focuses on 43

microorganisms that are capable of causing infection and disease in the human 44

population. Specifically it is applicable for assessing risk associat ed with ingestion of 45

foodborne pathogens ( in raw and processed foods)

, and water

- based or wate rborne 46Microbia l Risk Assessment Guideline Page 5

Draft

– Do not cite, circulate, or quote pathogens ( in drinking water, recrea tional water, wastewater, etc.). I could also be 1

applied to assessing risk of human pathogens in soil and solid wastes or transported 2

through the air, and human exposure to biological warfare agents. The Guideli ne may 3

also apply for other common forms of exposure including inhalation and dermal exposure 4

pathways. At present, oligonucleotides, prions, preformed microbial toxins, and other 5

submicrobial entities are not covered under the Guideline ow ing to a wide a rray of 6

unknowns associated with those agents. 7

8

This Guideline should help you account for differences between the general 9

population and different life stages and sensitive populations. It is flexible enough that 10

risks to individuals, subpopulations, and the general population can be dealt with using 11

either available static or dyna mic susceptibility models. The approaches and tools 12

provide d by this Guideline should be appropriate for typical human health related 13

concerns. The Guideline does not include criteria to specifically identify sensitive groups 14

or life stages

, because those groupings are specific to each risk assessment

, based on the 15

Agencies ‘ unique health protection concern and public health goal

. 16

17

The intent of the Guideline is to provide the necessary elements for successfully 18

conducting a full risk assessment

, quantitative or qualitative, including: 19

20

a) Planning and s coping including p roblem f ormulation

, 21

22

b) Hazard i dentification (HI)

, 23

24

c) Hazard c haracterization (HC)

, 25

26

d) Dose

- r esponse assessment

, 27

28

e) Expo sure a ssessment

, and 29

30

f) Risk c haracterization

. 31

32

This Guideline incorporates these elements and follows the Codex definition for 33

th e risk assessment process (see S e ction 1.1)

, except that the qualitative aspects of hazard 34

characterization are presented with h azard identification

, instead of with dose

- response 35

(Codex, 1999)

. It is intended that the Guideline will also assist risk assessors to meet the 36

needs and expectations of risk managers

, and provide appropriate and effective risk 37

communication with manager s, stakeholders, and the public. 38

39

1. 5 What are Some Fundamental Differences between Microb es and Chemical s ? 40

41

The diff

erences between chemical hazards and human su sceptibility factors 42

considered in chemical risk assessments and microbial

- host factors in MRAs can be few 43

or m any depending on the microbial antagonist, host resistance factors, and exposure 44

considerations. W hile these two types of risk assessments are c onceptually similar, there 45

are typically enough differences between chemicals and microorganisms that having a n 46Page 6 Microbia l Risk Assessment Guideline

approach specifically detailing unique microbial considerations is very useful

. Even 1

though the uniqueness of each chemical is considered indiv idually in chemical risk 2

assessment, s ome significant differences in MRAs to be considered include: 3

4

a) Microbial growth and death

– Pathogens increase and decrease in number in 5

the environment and host

. Different species, and even different strains within 6

a pathogenic species, grow and die in unique patterns. In contrast, although 7

chemicals can bioaccumulate

, bioconcentrate and undergo transformations

, 8

they do not multiply in the environment or hosts. Both chemicals and 9

pathogens can decrease due to enviro nmental factors ; chemicals can be 10

transformed or degrade d , and pathogens can die or become uncultura b l e but 11

may remain infectious

. Some decrease in chemicals may be reversible (e.g., 12

seques tered toxicants may remobilize)

. However, microbial toxins can rem ain 13

after the organism dies and some enterotoxins are heat stable and resistant to 14

degradation. 15

16

b) H ost immunity and susceptibility

– Although body weight, age, and 17

metabolic capacity differences are considered in chemical risk assessment

, 18

genetic and acquir ed differences in susceptibility are not considered in 19

chemical risk assessment in the same manner as in MRA

. Whereas 20

uncertainty factors or assessment using data from known sensitive populations 21

may be used to consider these host differences in chemical risk assessment, in 22

MRA immune status can be included in a dynamic model. The i mmune 23

system may be considered in chemical risk assessment if a chemical cause s a 24

hypersensitivity reaction. Infection and resulting illness due to pathogens is, 25

in some cases, highly dependent on the immune status of the individual, which 26

can fluctuate based on time since last exposure, presence of concurrent 27

infections, and a number of other factors (e.g., life stage

, nutrition , genetics). 28

29

c) Diversity of h ealth e ndpoints

– The same dose of a pathogen may result in a 30

broad range of health outcomes or endpoints depending on the characteristics 31

of the host and the exposure scenario. Endpoints for the same dose could 32

include: no effect, asymptomatic infection, mild or severe sympto ms, or 33

mortality for the most susceptible. The percentage of potential hosts in each 34

category fluctuates over time

. 35

36

d) Genetic diversity and evolution of microbial strains

– Microorganisms are 37

genetically diverse and allelic ratios in a population can chan ge significantly 38

within a few generations. In addition, microbial genomes can evolve quickly 39

(within days or weeks) through mutation or vertical gene transfer (within a 40

species) or horizontal gene transfer (between different species, families, and 41

higher taxonomic differences) . Strains of the same species (e.g., 42

Cryptosporidium parvum , E. coli ) can have multiple genotypes, poten tially 43

with different virulence for human hosts. Some pathogens (e.g., Helicobacter 44

pylori

, many viruses) behave like quasi

- spec ies, which are fluctuating 45

populations of genetically distinct variants that co

- exist within a single host 46Microbia l Risk Assessment Guideline Page 7

Draft

– Do not cite, circulate, or quote ( Boerlijst et al., 1996; Covacci and Rappuoli, 1998). Microbes represent a 1

―moving target

, ‖ because the distribution of strains and virulence factor s can 2

fluctuate rapidly in a given medi um

. Although there is great diversity among 3

chemicals, particularly considering mixtures, the mechanism of change is not 4

evolution. 5

6

e) Potential for secondary transmission

– Microbial infections can be 7

transmitted betwe en individuals, and even to some animals. With the 8

exception of the mother

- fetus relationship, chemicals in tissues of exposed 9

individuals are not known to transmit to other individuals. Chemicals that are 10

on an exposed individual ‘s clothing or skin can be transferred to household 11

and other inanimate objects (fomites) , but that transfer results in dilution of 12

the chemical, as opposed to pathogen secondary transmission, which can 13

amplify the pathogen. Some microbes can remain viable for days, weeks, or 14

mo nths on surfaces, which increases the potential for transmission. For some 15

pathogens humans can become asymptomatic chronic carriers and can infect 16

others and contaminate food and water sources with displaying symptoms 17

themselves for prolonged periods. 18

19

f) H eterogeneous spatial and temporal distribution in the environment

– 20

Pathogens are typically heterogeneous ly distributed in environmental 21

matrices. P athogen growth may lead to clustered distr i b u tions, and pathogen s 22

may physically clump together or may be e mbedded in or attached to organic 23

and inorganic particulate debris, making concentration determinations 24

difficult. Although concentration in pipe scale and biofilms is also a problem 25

for chemical contaminants, some pathogens can grow and/or be protected i n 26

these environments. Also, many types of pathogens occur only episodically 27

and typically can be found only during short

- lived disease outbreaks (i.e., 28

epidemics) in a community. Seasonal and event

- related (wet weather) spikes 29

are common. 30

31

g) S ingle exposur e health outcome

– C hemical risk assessment s are conducted 32

for ac ute exposures that cause immediate health outcomes and for chronic 33

exposures with long

- term health effects . For chronic exposure to chemicals

, 34

the risk may be from daily exposure over a 70

- y ear lifespan, whereas for 35

pathogens the risk may be from a single exposure with health effects 36

noticeable within days or weeks. Some p athogens may have sequelae, which 37

are health outcomes that appear much later than the original symptoms

. Some 38

sequelae a re chronic (long lasting)

. Unlike the long

- term exposures often 39

considered for chemicals, longer

- term risks due to pathogen exposure are not 40

typically considered for MRA

. 41

42

h) Wide range of microbial response to interventions

– Many risk assessments 43

address r isks to human health associated with media that have been subjected 44

to some sort of treatment (such as wastewater treatment for water or strict 45

processing of foods). Microorganisms respond with wide variability to 46Page 8 Microbia l Risk Assessment Guideline

environmental and treatment factors. For example, response to drinking water 1

treatment needs to be taken into account when comparing microbial levels in 2

ambient water and treated drinking water . However, in the Clean Water Act 3

304(a) a mbient water quality criteria, EPA makes a policy assumption that 4

drinking water treatment has no effect on chemical concentration when they 5

determine what levels to set for ambient water (EPA, 2000 c ). 1 6

7

i) Detection method sensitivity

– Microbial d etection methods may not be 8

sensitive enough to detect pathogens at a level of regulatory concern. This is 9

not necessarily the case for all pathogens or all media, but does apply to some 10

combinations of organisms and media . Theoretically, a single pathogenic 11

organi sm can cause infection and lead to illness . Analytical met hods for 12

detecting low levels of pathogens (e.g., one organis m in 1000 liters of water) 13

are not sufficiently developed to be reliable. In short, the human body is a 14

more sensitive detector of pathogens than many laboratory methods. In 15

addition

, the activ e or viable

- but

- not

- culturable (VBNC) state is not de tectable 16

by traditional culture

- based labo ratory methods (see Section 3.8 ) 17

18

j) Population, community, and ecosystem

- level dynamics

– M icrobial 19

pathogens have complex interactions with other members of their species, 20

other species, and the abiotic environment. For example, pathogens compete 21

with non

- pathogens for resources, and many non

- viral human pathogens have 22

animal hosts that can greatly complicate the ecological dynamics of pathogen 23

occurrence. 24

25

k) Routes of e xposure

– Many r outes of exposure are similar for chemicals and 26

microorganisms ; however

, there are some potentially important differences

. 27

Dermal exposure may be important for some chemicals but not necessarily 28

with microbial exposure since unbroken skin is a natural barrier for entry. 29

However

, dermal contamination with pathogens can lead to oral exposure via 30

transfer to consumed food or water. Other aspects may include consideration 31

of direct person

- to

- person or person

- to

- environment

- to

- person route s. 32

33

These factors are significant considerations in MRA, but the approach for dealing with 34

each one may be unique to a particular risk assessment scenario

. 35

36

1. 6 What is the Relationship of Infectious Disease to Human Health a s Applied 37

in a MRA ? 38

39

Understanding the relationships and interactions between a microbial pathogen, 40

its host, and the exposure to the pathogen in the environment is key to determi ning the 41

potential health impact a pathogen will have on an individual or population. The 42

epidemiological triangle (disease triad) illustrates the inter

- relationship between the host, 43

pathogen, and environment components (Figure 1. 2) . For each of the com ponents

, there 44

are several key factors that may be considered. In many cases, a comprehensive 45

1 Except for disinfection byproductsMicrobia l Risk Assessment Guideline Page 9

Draft

– Do not cite, circulate, or quote quantitative treatment of all the factors to conduct a MRA is not possible because of data 1

limitations. However, t he factors can be at least considered qualitat ively . The various 2

points of the disease triad will be discussed throughout this G uideline. 3

Figure 1. 2 The Epidemiological Triangle 4

5

6

1. 7 What are the Benefits of Iterative MRA ? 7

8

There is g eneral recognition by risk assessors that an iterative approach is 9

necessary when conducting a MRA , so that there is opportunity to modify the risk 10

assessment based upon changes in data availability and evolving agency policies 11

( Presidential/Congressional Committee [ P/CC ], 1997)

. Often, the lack of data, new data 12

or interpretations, or uncertainty or variability in information will requi re you t o revisit 13

the original charge or premise for conducting a risk assessment. A s revisions are 14

conducted

, new infor mation can be incorporated , especially i n areas found to be the most 15

important to assessing risk

. Th is ensure s that the risk manager is provided with the most 16

accurate interpretation of risks

, so that appropriate management decisions can be made

. 17

Risk co mmunication strategies can also be developed in parallel with risk assessment 18

iterations. You should be prepared to be involved in the risk communication process as 19

needed (see Chapter 8). 20

21

1. 8 How Does This Guideline Fit in with My Agency’s Current MRA 22

Approaches and Practice s ? 23

24

This G uideline is not intended to supplant your Agency‘s prerogative in 25

establishing the approach that you may follow f or preparing Agency

- wide or even Office

26

wide MRA procedures. The goal of this MRA G uideline is to provide an overarching 27

framework and a n approach to conducting MRAs that ar e flexible and broadly defined

, so 28

that any Agency can utilize them in preparing m ore tailored approaches to MRA . This 29

G uideline is not intended to preferentially take the place of your Agency‘s own risk 30

assessment approach, protocol, or guideline, but should help promote consistency in 31

MRA among agencies . Each Agency has its own uniq ue regulatory requirements, legal 32

requirements, policies, and conventions that must be considered in their risk assessments. 33

Ideally, t his interagency G uideline is intended to provide the general framework that can 34

be incorporated into the more specific M RA guidance that your Agency prepares. 35

However, it is up to each Agency to make the determination of how or whether they 36

utilize this guideline , consistent with inter national and national scientific guidance (e.g., 37

Codex Alimentarius and the United States ‘ National Academies)

. 38

39 host

pathogen environmentPage 10 Microbia l Risk Assessment Guideline

1. 9 How is this MRA Guidance R elated to O ther MRA Frameworks/Guidelines 1

that are C urrently A vailable? 2

3

There a re two major sets of frameworks that influenced the development o f this 4

Guideline. The first set of frameworks include s the NRC 1983 report ( NRC, 1983) and 5

the NRC Science and Decisions: Advancing Risk Assessment (NRC, 200 9 )

, which are 6

related because the NRC 200 9 framework is an enhancement of the NRC 1983 report. 7

The NRC frameworks are geared to chemical risk assessment, but have applicability to 8

MRA. They are also broadly applicable for many different exposure media. The second 9

set of frameworks is t he Codex Alimentarius Commission ( Codex ), Principles and 10

Guidelines for the Conduct of Microbiological Risk Assessment ( Codex , 1999) and Codex 11

Proposed Draft Principles and Guidelines for the Conduct of Microbial Risk 12

Management ( MRM ) ( Codex , 200 7 a ). The Codex framework is specific to the food 13

media, but is tailored to microbial hazards. This Guideline is broadly applicable to many 14

media, but is tailored to microbial hazards to humans , so both sets of frameworks were 15

important for the development of this Guideline. These two sets of frameworks were not 16

the only sources consulted for development of this Guideline. 17

18

U.S. government agencies (e.g., EPA, USDA, FDA, DHS, and DOD)

, as well as 19

government organizations from other countries (e.g., Canada, Europea n Union, New 20

Zealand) and international Agencies (e.g., WHO, FAO/CODEX, and the Organization for 21

Economic Cooperation and Development [ OECD ]

– for example , see FAO/WHO, 2006)

, 22

have prepared various levels of guidance to support MRA applications. A n EPA

23

sp onsored study

, Foundations and Frameworks for Human Microbial Risk Assessment 24

(Parkin, 2008 )

, present ed an extensive search and evaluation of frameworks available for 25

use in conducting MRA. The study determined that there were four general categories of 26

f rameworks that have been applied in MRA: 27

28

a) The 1983 NRC report 29

30

b) A m odified NRC 1983 approach without an explicit problem formulation (or 31

planning and scoping) step 32

33

c) A m odified NRC with a problem formulation (or planning and scoping) step 34

35

d) The International Life Science Institute (ILSI) approach (in association with the 36

EPA‘s Office of Water) developed for water

- based media (ILSI, 2000) 37

38

While most microbial risk assessors recognize shortcomings with utilization of a 39

uniform, exclusive application of the N RC 1983 approach, most have not explicitly 40

attempted to take a completely fresh look at approaches to conduct MRA except for the 41

ILSI ( 2000) approach which was loosely based upon EPA‘s draft Ecological Risk 42

Assessment Guidelines. 43

44

EPA, FDA, USDA, DOD, an d DHS have utilized their own unique approaches to 45

conducting risk assessments, but it is important to keep track of other federally mandated 46Microbia l Risk Assessment Guideline Page 11

Draft

– Do not cite, circulate, or quote requirements that may apply to MRA. For example, when relevant, Executive Orders 1

and OMB memorandum s apply to MRA (EPA, 2002b; OMB, 2007b; Presidential 2

Memorandum, 2009; OSTP, 2010)

. 3

4

1.1 0 What are Major Principles for M RA ? 5

6

The principles in Text Box 1.1 are adaptable to microbial hazards in media other 7

tha n food

. However , different agencies may have different practices regarding how risk 8

management and risk assessment activities are separated and the principles are framed in 9

reference to food safety risks

. Some agencies have more integrat ed management and 10

assessment. In cases where the risk management and risk assessment functions are more 11

integrated

, transparency about how and where risk management is integrated in risk 12

assessment is crucial. The NRC report Science and Decisions: Advancing Risk 13

Assessme nt proposed that EPA should adopt: 14

15

―... a framework for risk

- ba sed decision making. The framework 16

consists of three phases: I, enhanced problem formulation and 17

scoping, in which the available risk

- management options are 18

identified; II, planning and assessme nt, in which risk

- assessment 19

tools are used to determine risks under existing conditions and 20

under potential risk

- management options; and III, risk 21

management, in which risk and nonrisk information is integrated to 22

inform choices among options. ‖ (NRC, 200 9 ) 23

24

The 200 9 NRC framework also has a functional integration of risk assessment and 25

risk management within a risk

- based decision framework

. 26

27

Text Box 1.1 General P rinciples of MRA

The ten general principles of MRA provided by the international Codex framew ork for

microbiological hazards in food are below, as stated in Codex (2007 b ). The original

numbering from the document is maintained. Principles 1

- 19 are general risk analysis

principles and policy principles and are not included in this excerpt.

20. E ach risk assessment should be fit for its intended purpose.

21. The scope and purpose of the risk assessment being carried out should be clearly

stated and in accordance with risk assessment policy. The output form and possible

alternative outputs of the r isk assessment should be defined.

22. Experts involved in risk assessment including government officials and experts from

outside government should be objective in their scientific work and not be subject to any

conflict of interest that may compromise the integrity of the assessment. Information on

the identities of these experts, their individual expertise and their professional experience

should be publicly available, subject to national considerations. These experts should be

selected in a transparent m anner on the basis of their expertise and their independence

with regard to the interests involved, including disclosure of conflicts of interest inPage 12 Microbia l Risk Assessment Guideline

connection with risk assessment.

23. Risk assessment should incorporate the four steps of risk assessment, i.e. hazard

identification, hazard characterization, exposure assessment and risk characterization.

24. Risk assessment should be based on scientific data most relevant to the national

context. It should use available quantitative information to the greate st extent possible.

Risk assessment may also take into account qualitative information.

25. Risk assessment should take into account relevant production, storage and handling

practices used throughout the food chain including traditional practices, methods of

analysis, sampling and inspection and the prevalence of specific adverse health effects.

26. Constraints, uncertainties and assumptions having an impact on the risk assessment

should be explicitly considered at each step in the risk assessment and docu mented in a

transparent manner. Expression of uncertainty or variability in risk estimates may be

qualitative or quantitative, but should be quantified to the extent that is scientifically

achievable.

27. Risk assessments should be based on realistic expos ure scenarios, with consideration

of different situations being defined by risk assessment policy. They should include

consideration of susceptible and high

- risk population groups. Acute, chronic (including

long

- term), cumulative and/or combined adverse he alth effects should be taken into

account in carrying out risk assessment, where relevant.

28. The report of the risk assessment should indicate any constraints, uncertainties,

assumptions and their impact on the risk assessment. Minority opinions should a lso be

recorded. The responsibility for resolving the impact of uncertainty on the risk

management decision lies with the risk manager, not the risk assessors.

29. The conclusion of the risk assessment including a risk estimate, if available, should

be pre sented in a readily understandable and useful form to risk managers and made

available to other risk assessors and interested parties so that they can review the

assessment.

1

2

An overarching principle for MRA in this G uide line is to provide a systematic 3

a pproach to the consideration of all information, models, and other tools that allow a 4

suitable examination of the relationship of a microbial pathogen and human exposure to 5

specific health endpoints and levels of effect. The process may be iterative, with 6

increasing quality of data to reduce uncertainties with each iteration (see section 2.5.1)

. 7

Details of this systematic approach will be covered in later chapters. The other major 8

principles you can consider in conducting risk assessments are ― transparen cy

, clarity

, 9

consistency , and reasonableness ‖ (TCCR ) ( EPA, 2000a). 10

11

a) Transparency is the foremost principle of the four because when followed it 12

should help satisfy the other principles

, in that it will lead to clarity and allow the 13

evaluation of consistenc y and reasonableness. To provide transparency, the 14

methods used and assumptions should be clear and understandable to the intended 15

recipients of the assessment. From this, the audience for the risk assessment 16Microbia l Risk Assessment Guideline Page 13

Draft

– Do not cite, circulate, or quote should be able to assess the adequacy of the data and methods used to provide the 1

information. It also means that conclusions drawn from the risk assessment and 2

associated research that are science

- based are separate from subsequent policy 3

judgments and risk management decisions, and the use of meth ods, models, 4

assumptions and defaults are clear. 5

6

b) Clarity means the manner in which the risk assessment is presented, such as how 7

it is written and the appropriate use of illustration s or graphics to portray the 8

findings. A main goal of this G uid elin e is t o enable production of documents 9

available for public scrutiny that are easily comprehendible and in simple 10

language. 11

12

c) Consistency provides a context for the reader that can be readily understood for 13

comparison to other similar documents and whether it pr ovides harmony with an 14

Agency‘s policy, procedural guidance, and scientific rationale. Where there are 15

differences or inconsistencies, an explanation of why different approaches or 16

interpretations were used and why the conclusions differ can be provided. 17

18

d) Reasonableness provides the audience with knowledge about the extent that 19

professional judgments and assumptions are well founded. This can also address 20

plausibility and comparisons against other similar types of risk assessment 21

findings. 22

23

While these ar e the major underlying principles, other factors are also important

, 24

such as the assessment of data quality, data analysis, and peer review. Most Agencies 25

have addressed their needs for these in separate Agency guidelines and all have adopted 26

OMB Informat ion Quality guidelines relevant to these issues (OMB, 2002)

. 27

28

The Office of Science and Technology Policy (OSTP) , directs that, ― When 29

scientific or technological information is considered in policy decisions, the information 30

should be subject to well estab lished scientific processes, including peer review where 31

appropriate, and each agency should appropriately and accurately reflect that information 32

in complying with and applying relevant statutory standards ‖ (Presidential Memorandum, 33

2009; OSTP, 2010). 34

35

In 2007

, OSTP and OMB reissued the 1995 Principles for Risk Analysis (OMB, 36

2007b). The six principles pertaining to risk assessment include: 37

38

1. Agencies should employ the best reasonably obtainable scientific information to 39

assess risks to health, safety, and the environment. 40

2. Characterizations of risks and of changes in the nature or magnitude of risks 41

should be both qualitative and quantitative, consistent with available data. The 42

characterizations should be broad enough to inform the range of policies to re duce 43

risks. 44Page 14 Microbia l Risk Assessment Guideline

3. Judgments used in developing a risk assessment, such as assumptions, defaults, 1

and uncertainties, should be stated explicitly. The rationale for these judgments 2

and their influence on the risk assessment should be articulated. 3

4. Risk assessments should encompass all appropriate hazards (e.g., acute and 4

chronic risks, including cancer and non

- cancer risks, to human health and the 5

environment). In addition to considering the full population at risk, attention 6

should be directed to subpopulations th at may be particularly susceptible to such 7

risks and/or may be more highly exposed. 8

5. Peer review of risk assessments can ensure that the highest professional standards 9

are maintained. Therefore, agencies should develop policies to maximize its use. 10

6. Agencie s should strive to adopt consistent approaches to evaluating the risks 11

posed by hazardous agents or events. 12

13

These principles have been incorporated throughout this Guideline. 14

15Microbia l Risk Assessment Guideline Page 15

Draft

– Do not cite, circulate, or quote 

## 2. PLANNING AND SCOPING 1

2

Planning and scoping will help ensure that a risk assessment is relevant and well 3

done. The NRC 200 9 framework recommended ―increased attention to the design of risk 4

assessment in its formative stages [and] that planning and scoping and problem 5

formulation, as ar ticul ated in EPA guidance documents (EPA

, 1998 a , 2003), should be 6

formalized and implemented in EPA risk assessments‖ (NRC

, 200 9 ). Rigorous 7

preparation is needed at the start of the risk assessment process

, to facilitate 8

communication during and following the risk assessment and t o ensure that all issues are 9

sufficiently vetted, all participants are clear on the objectives and goals, and managers are 10

clear about the commitment of personnel and other resources (EPA, 1992; NRC, 1996)

. 11

12

For more detailed de scriptions of the usefulness and implementation of the 13

planning and scoping process, the FDA, USDA, and EPA sources provide general 14

information on how to proceed (FDA, 2002; USDA, 2003; EPA, 2000a, 2002 a ; NRC 15

200 9 ). This chapter provides an overview of th is first step in the risk assessment process. 16

Several interesting case studies from ecological risk assessment are presented in EPA‘s 17

Lessons Learned on Planning and Scoping for Environmental Risk Assessments (EPA, 18

2002 a ). 19

20

2.1 What is Planning and Scopin g? 21

22

Planning and scoping can be viewed as a process that defines the purpose and 23

scope of a risk assessment and focuses the issues and approach(es) involved in 24

performing the assessment. A clearly arti culated purpose and scope provides a sound 25

foundation for later judging the success of the risk assessment and for an effective risk 26

characterization. In a sense, the planning and scoping process lays out a ―road map‖ for 27

how the risk assessment will be a ccomplished. 28

29

EPA considers Planning and Scoping steps to be as follows (based on EPA, 30

2003 a

, 200 4 b ): 31

32

a) D efining the purpose of the assessment 33

b) D efining the scope of analysis and products needed 34

c) A greeing on participants, roles and responsibilities 35

d) A greeing on depth of assessment and analytical approach (for example, will the 36

risk assessment include static or dynamic modeling) 37

e) A greeing on resources available and schedule 38

f) F ormu l a ting the problem (see Section 2.1.1) 39

g) D eveloping the conceptual model (see Section 2.6.3) 40Page 16 Microbia l Risk Assessment Guideline

h) C onstructing the analysis plan (see Section 2.6.4 ) 1

See Section 2.3 for an overview of how CFSAN does planning and scoping for major risk 2

assessments. 3

2.1.1 What is Problem Formulation? 4

5

The pro blem formulation exercise is a discussion and analysis activity that 6

involves all relevant parties including the risk manager, risk assessment team, risk 7

communication specialist and

, when appropriate

, relevant stakeholders and interested 8

parties. The out puts of the problem form ul ation are: 9

10

a) A d efinition of the valued entity and endpoint: what is the entity that should be 11

protected and what are the undesirable effects that you are trying to avoid. For 12

MRA this may involve a policy determination of what th e ―valued entity‖ is (e.g., 13

general population, young children, pregnant women, immunologically 14

compromised) and what is considered to be an appropriate level of protection 15

(ALOP) against infection or disease

. 16

17

b) A Conceptual M odel that lays out the anticipa ted exposure scenarios of the 18

microorganisms from the source to the receptor. With MRA this may be the 19

movement of enteric pathogens from some source (e.g., treatment works, manure 20

application to a field, critical point in the food processing system) to t he 21

sub population of concern and highlights various hazardous events that may lead 22

to increased risk and where risk management may be most effective

. 23

24

c) An Analysis P lan which provides a road map for addressing the problem. In 25

short, it is analogous to an ex perimental design. In the analysis plan risk 26

hypotheses generated earlier are examined and discussed; the relationships 27

between pathways and valued entities are further examined; and the level of 28

precision and data quality are considered in light of avail able information. 29

30

2.2 What are the Benefits of Planning and Scoping? 31

32

The planning and scoping process hel ps everyone involved in th e risk assessment 33

understand how the risk assessment fit s into the overall decision making process

. 34

Planning and scoping promotes : 35

36

a) initial planning to identify appropriate timelines and necessary resources, thereby 37

improving efficiency ; 38

39

b) agreement among principle parties regarding the goals, commitment, t ime

- frame 40

and resources , by setting realistic expectations ; 41

42

c) the prospect of less unanticipated controversy

, since all interested parties 43

contribute, areas of disagreement can be dealt with upfront, not left as a surprise 44

at the end ; 45Microbia l Risk Assessment Guideline Page 17

Draft

– Do not cite, circulate, or quote 1

d) identification of and participation by those from many disciplines (e.g., 2

microbiologists, toxicologists, economists, lawyers) to help in the process thereby 3

ensuring that each risk assessment and characterization is useful for the intended 4

audience(s) ; 5

6

e) an understanding of th e degree of complexity needed in the risk assessment to 7

adequat ely inform the decision at hand ; and 8

9

f) be tter informed decisions

. 10

11

2.3 What do I Consider When Deciding to Initiate a MRA? 12

13

Unless your agency is required to conduct a particular risk assessment, managers 14

need to decide whether a risk assessment is appropriate, feasible and will actually be 15

performed. This decision is commonly made during planning and scoping. Deciding not 16

to initiate a risk assessment may be an acceptable outcome of planning and scoping. The 17

agency may decide that a decision can be reached without conducting a risk assessment 18

or other priorities or risk assessments may take precedence. There are sever al criteria 19

you can consider for identifying a candidate risk assessment: 20

21

a) Characteristics and importance of the hazard (s) of concern 22

23

b) Magnitude (presence, prevalence, concentration of hazards) and severity (impact 24

on public health) of the risk 25

26

c) Urgency of the situation 27

28

d) Subp opulations of concern 29

30

e) Other factors associated with specific hazards (such as water treatment processes, 31

food processing, cooking, cross contamination) 32

33

f) Availability of resources (time, money, staff) 34

35

A risk assessment may be required to comply with regulatory analysis requirements 36

(OMB

, 2003). T he Office of Information and Regulatory Affairs has provid ed a checklist 37

to assist agencies in producing regulatory impact analyses (RIAs), as required for 38

economically significant rules by Exec utive Order 12866 and OMB Circular A

- 4 ( E.O. 39

12866, 1993; OMB, 2010; OMB, 2003)

. Appropriate assessments of risk may be 40

necessary to address international trade agreements (e.g., World Trade Organization 41

Sanitary and Phytosanitary Agreement) . E nsuring t hat each assessment of risk is fit for 42

its intended purpose and is based on scientific data most relevant to the national context 43

ensures that the effort and scope of the assessment of risk are appropriate for the risk 44

management questions being raised so that practical risk management options can be 45

formulated. 46Page 18 Microbia l Risk Assessment Guideline

1

Other s pecific examples of when a risk assessment may be appropriate include: 2

3

a) Review of the reliability or utility of a standard 4

5

b) Cases where the current standard is inconsistent with other gove rnment policies, 6

guidelines, or thresholds 7

8

c) Cases where an agency has been petitioned for a regulatory action 9

10

d) Establishment of standards for regulatory action 11

12

e) Evaluation of the public health implications of different tolerable risk levels 13

14

f) Cases where a data gap analysis is desired 15

16

g) Cases where the hazard is a serious health issue, emerging pathogen and/or public 17

health concern 18

19

h) The exposure system is complex. 20

21

FDA‘s Center for Food Safety an d Applied Nutrition ‘s (CFSAN) Initiation and 22

Conduct of All “Maj or” Risk Assessments within a Risk Analysis Framework outlines 23

how CFSAN selects, conducts, and communicates food safety risk assessments (FDA, 24

2002). CFSAN‘s process for identifying and selecting major risk assessments is divided 25

into four phases (FDA, 2 002): 26

27

a) Phase 1: Concept Generation

– Collect ideas and maintain a list of potential risk 28

management questions for which a risk assessment would assist with policy 29

decisions. Develop justification for candidate risk assessments, including, 30

purpose of asses sment, scope of problem, importance to the Center, and how the 31

result will be used by the Center. 32

33

b) Phase 2: Problem Identification

– The candidate risk assessment and supporting 34

information (justification) are reviewed to determine whether the assessment 35

m eets the Center‘s needs. This phase results in one of three recommended 36

actions, conduct data feasibility study, not required for regulatory decision, or 37

more information needed to make decision. 38

39

c) Phase 3: Data Feasibility (Evaluation and Recommendation)

– Information is 40

collected and reviewed to determine availability of data needed to answer risk 41

assessment question(s). This phase results in one of four recommended actions, 42

conduct quantitative risk assessment, conduct qualitative risk assessment, more 43

research needed, or modify question and conduct alternative assessment. 44

45Microbia l Risk Assessment Guideline Page 19

Draft

– Do not cite, circulate, or quote d) Phase 4: Disposition (Selection)

– Using the results from the data feasibility 1

determination as an aid, risk assessment(s) to be conducted are selected. Decision 2

is based on technica l merit, resource availability, the Center‘s priority needs, and 3

other legitimate factors. 4

5

2.4 Who C an b e Involved with Planning and Scoping? 6

7

All interested parties could be involved with planning and scoping , but, f rom a 8

practical standpoint

, bring ing everyone together at one time and discussing all aspects of 9

the effort at once may be difficult and therefore would probably include more than a 10

single session . Major participants inclu de relevant risk managers, risk assessors

, and 11

other members of the ― team ‖ working on the decision that needs to be made. In addition 12

to the appropriate microbiologists, infectious disease experts and/or individua ls trained in 13

infection control and other members such as economists, lawyers, engineers, policy 14

makers, and communicators should be included . A risk assessment dialogue among the 15

risk managers, risk assessors, risk communicators, economists, and other technical 16

experts should develop the broad d imensions and elements of the risk assessment, the 17

management questions and goals for the assessment, a tentative budget and schedule, and 18

an approach for conducting the risk assessment. The risk assessor, risk manager, and 19

planning team interactions may vary depending on the Agency. Generally, they meet to 20

evaluate and select the kind of risk information, exposure scenarios, and assessment 21

issues to be covered. Risk assessor s should be very involved in planning and scoping at 22

every stage

, because risk m anagers will have technical and data related questions that 23

m a y require your input. As a risk assessor, y ou will want to weigh in on whether the 24

tools and data available can answer the questions being posed. Planning and scoping 25

would not be successful w ithout the technical input of risk assessors and the management 26

perspectives from risk managers. In addition, the risk manager should consider stating 27

explicitly any reasons to limit the technical scope of the assessment, and you should 28

consider including details on resource limitations , data availability and quality, and 29

methods availability. In other words, you can be very clear and transparent about what 30

you plan to include in the risk assessment and what you plan to exclude from the risk 31

assessment an d why. Further elaboration on the role of risk assessors is found in Section 32

2. 8

. 33

34

Agencies with a hierarchy of risk managers will have a hierarchy of risk 35

management activities. Planning and s coping can outline the management hierarchy with 36

a degree of detail that is appropriate for planning deliverables and milestones. The roles 37

are not exclusive to each level and in practice would likely overlap considerably. Risk 38

managers at all levels are responsible for ensuring appropriate communication with 39

mana gers above and below their level. It is important to identify the team leader and 40

his/her basic responsibilities at the outset to avoid misunderstandings as the process 41

develops. Risk managers are generally the decision makers in their organization. The 42

top manager is the ultimate decision maker for his/her organization and is accountable for 43

both the risk characterization process and products in his/her office. However, every risk 44

manager may make decisions appropriate for their level in the management hierarchy. 45Page 20 Microbia l Risk Assessment Guideline

You should be prepared to communicate with many levels of managers particularly if you 1

are working on a high profile risk assessment. 2

3

The importance of s takeholder involvement during the planning and scoping 4

process depen ds upon the nature of the problem, their interest, and ability to contribute. 5

Stakeholders can be identified e arly in the planning and scoping of the risk assessment

. 6

How stakeholders can be involved most effectively needs to be decided on a case

- by

- case 7

basis

. Public invol vement

, early and often

, leads to a much clearer risk assessment 8

product. This involvement also allows for flexibility and buy

- in for future decision 9

making if the need arises to deviate from the original plan. 10

11

Affected parties can share their points of view about the risk and how it could be 12

managed. Their input is particularly helpful in determining what should be included in 13

the assessment, how they might be affected or exposed to the risk, and what additional 14

data or exposure scenarios should be deve loped

. 15

16

You should determine if your agency has any stakeholder involvement guidance 17

and integrate those practices into planning and scoping. Stakeholder involvement 18

practices may be well developed and formalized or haphazard and inconsistent. Building 19

the necessary relationships with stakeholders to maintain dialogue takes considerable 20

effort, but this should not deter you and risk managers from engaging in this important 21

activity. Risk communication specialists should be engaged in develop ing stakeho lder 22

involvement plans. The National Academies of Science report, Public Participation in 23

Environmental Assessment and Decision Making , is a resource for considerations when 24

engaging stakeholders (NRC, 2008). 25

26

2.5 How C an the MRA be Used ? 27

28

Although risk assessments conducted by different agencies are not used for the 29

same purposes , all agencies perform risk assessments with one or more of the following 30

goals in mind (adapted from U.S. Army Center for Health Promotion and Preventive 31

Medicine [ USACHPPM ] , 2009) : 32

33

a) To mitigate, e.g., to mitigate adverse effects or risk f rom a specific event 34

35

b) To confirm, e.g., to determine if regulations, policies, standards, criteria, and/or 36

goals are adequate 37

38

c) To decide whether and/or how to regulate, e.g., as needed to establish regulations, 39

policies, standards, criteria, and/or goals 40

41

d) To investigate, e.g., to determine research or other requirements that would 42

enhance predictive and/or risk ranking capabilities, or facilitate completion of 43

screening or feasibility assessments 44

45Microbia l Risk Assessment Guideline Page 21

Draft

– Do not cite, circulate, or quote Depend ing on the risk assessment‘s purpose, a particular assessment approach 1

may be employed. The appropriate risk assessment approach for a specific risk 2

management problem or decision depends on the question(s) that need to be answer ed 3

and the availability of data. For example, if adequate data are available, a quantitative 4

risk assessment is possible ; if fewer data are available, a qualitative assessment or a data 5

gap analysis (discussed in Sect ion s 2. 6.5 a nd 6.4 ) may be more appropriate. 6

7

2.5.1 What “Depth” C an I g o i nto in the Risk Assessment? 8

9

A major consideration for the risk assessment approach is how much detail or 10

―depth‖ you need to incorporate to address the risk management question(s) or decision. 11

D ue to various management needs, the risk assessment approach is not necessarily a one

12

size

- fits

- all approach. This guidance is intended to provide flexible methods fo r 13

supporting different types of assessments (screening, safety) and outputs (qualitative or 14

quantitative), as described in the American Society for Microbiology (ASM) Press book 15

(Schaffner, 2008). Guidance from Codex Alimentari us (1999) and World Organiza tion 16

for Animal Health (OIE, 1999) described qualitative and quantit at ive outputs as equally 17

valid (Wooldridge, 2008). Woodridge ( 2008) provides detailed discussion of qualitative 18

and quantitative assessments, and other risk analysts (Dennis et al., 2008) discuss both 19

estimates of risk and safety, mortality for listeriosis and allowable ('safe') levels of Vibrio 20

in seafood, respectively. 21

22

Be aware that the terms referring to the different types of assessments are 23

specifically defined in different contexts ; care should be used when ―naming‖ or referring 24

to types of assessments. For example, this G uideline is specific to the selection and 25

conduct of risk assessment, not a safety assessment. One difference is that risk 26

assessment estimates the likelihood and /or frequency of adverse health outcomes 27

resulting from an exposure and, in some cases, sources of risk and quantitative reductions 28

in risk based on various interventions. While much of this g uideline is relevant to a 29

safety assessment, a safety assessmen t may estimate the likelihood and/or frequency of 30

exceeding a specified threshold of concern (e.g., predetermined regulatory limits or 31

standards), or provide a determination of what is ― safe ‖ based on the conventions of the 32

standard

- setting procedure . For example , Codex standards traditionally specify 33

maximum limits for additives and residues in foods based on the concept of ―no 34

appreciable risk‖ and for contaminants in foods based on the concept of ―as low as 35

reasonably achievable‖ ( FAO/WHO, 1997 )

. Risk

- related terms in some statutes have 36

formal definitions. 37

38

There are many cases where you will likely need to perform a screening risk 39

assessment versus a fully developed one. This is usually done when a very time critical 40

decision is needed (e.g., quick mi tigation action is required after an event; im minent 41

exposure to a microbial hazard is identified). Screening risk assessments often provide a 42

conservative, health

- protective, estimate of possible risk that is based on the more 43

readily available data. I t is likely that you will resort to default assumptions to bridge 44

data gaps that you cannot wait for research to fill. It is important to be transparent about 45

the amount of uncertainty in your screening estimate and discuss whether the uncertainty 46Page 22 Microbia l Risk Assessment Guideline

causes underestimates or overestimates of risk based on the assumptions applied

. This 1

may be followed up by a more detailed assessment that will need to be conducted or risk 2

managers will need to take action(s). Thus , you also may be asked to return to the 3

asse ssment and refine and recalculate your estimates based on further gathering of data 4

with more time allotted (e.g., quick sampling assays, use of surrogate data, expert 5

elicitation). Alternatively , there may be a risk management decision to conduct a full risk 6

assessment. 7

8

A comprehensive ( ―major‖ ) risk assessment requires a substantial commitment of 9

resources. Thus, this depth of risk assessment is not appropriate when risk managers do 10

not need this level of sophistication to make a decision. Circumstanc es that may not 11

warrant a quantitative risk assessment would include, for example, a risk that is well 12

described by definitive data, a problem that is relatively simple, or an issue that is not of 13

regulatory concern. However, a comprehensive risk assessme nt is a powerful tool to help 14

risk managers evaluate and interpret information when the data describing a hazard are 15

incomplete, the exposure system is complex, or the issue is of high regulatory or 16

stakeholder concern. Details for a major risk assessment are presented in this g uideline. 17

18

Risk assessments can be either qualitative or quantitative in their description of 19

the likelihood of adverse health effects, depending on the extent of the data and 20

knowledge available, the existence of models or other to ols for quantitative predictions, 21

the complexity of the problem, the scope and nature of the question(s) posed by the risk 22

managers, and the time available to conduct the assessment. In quantitative assessments, 23

the risk is expressed as a mathematical sta tement of the probability of illness or death 24

after exposure to a specific hazard, and it represents the cumulative probabilities of 25

certain events happening and the uncertainty associated with those events. Conversely, 26

qualitative risk assessments use ve rbal descriptors of risk and severity as well as 27

uncertainty, and often involve the aggregation of assumptions

. 28

29

2.5.2 What Are Examples of Types of MRA ? 30

31

Risk assessments can take various forms depending on the agencies‘ needs. 32

Examples of risk assessment types that you are likely to use include risk ranking, product 33

pathway, risk

- risk, geographic

, and for sustainability assessments : 34

35

a) Risk ranking

– Risk ranking assessments compare the relative risk among 36

several hazards. For example, this type of assessment might involve a single 37

pathogen associated with multiple foods, a single food that has multiple 38

pathogens, or multiple pathogens and multiple foods. Risk ranking assessments 39

can help establ ish regulatory program priorities and identify critical research 40

needs. The Food and Drug Administration/U.S. Department of Agriculture 41

(FDA/USDA) Listeria monocytogenes risk assessment is an example of a risk 42

ranking assessment (FDA/ USDA/CDC , 2003). 43

44

b) Pro duct pathway analyses

– In product pathway assessments, the factors that 45

influence the risk associated with specific vehicle/hazard pairs are examined. For 46Microbia l Risk Assessment Guideline Page 23

Draft

– Do not cite, circulate, or quote food, it ideally starts at the farm and ends with consumption. This type of 1

assessment technique h elps identify the key factors that affect exposure including 2

the impact of potential mitigation or intervention strategies on the predicted risk. 3

The FDA Vibrio parahaemolyticus risk assessment is an example of a product 4

pathway analysis (FDA, 2005). 5

6

c) Ris k

- risk

– In risk

- risk assessments, a trade off of one risk for another is 7

considered, i.e., reducing the risk of one hazard increases the risk of another. An 8

example of this would be a determination of the impact on public health by 9

treating drinking wate r with a chemical (risk to chlorine exposure) versus the 10

impact of exposure to pathogenic organisms in water that is not treated. 11

12

d) Geographic

– In a geographic risk assessment, the factors that either limit or 13

allow the risk to occur in a given region are examined. The risk of introduction of 14

disease agents through water, air, food animals, or animal products in the U.S. 15

(e.g., intentionally as in a bioterrorism act or unintentionally) can be examined. 16

For example, the risk of introduction of bovine spong iform encephalopathy (BSE) 17

into the U.S cattle herds and the subsequent risk of variant Creutzfeldt

- Jacob 18

Disease (vCJD) in humans by the transmission from cattle through meats and 19

animal product pathways might be examined using a geographical approach. 20

21

e) M RA within sustainability assessments

– Using a systems

- level assessment 22

over the life

- time of its technical components, sustainability assessments attempt 23

to account for human health, ecosystem health

, and economic considerations. The 24

human health aspects include chemical and MRAs . The difference here is to 25

include the MRA over the expected life

- time of the technical system and via all 26

exposure pathways of pathogens to humans (e.g.

, drinking water, reuse waters, 27

aerosols pathways, recreational exposures

, a nd contaminated soils/foods )

. 28

29

2.5.3 What are Threat and Vulnerability Assessments for High

30

Consequence Biological Events? 31

32

This Guidance is pri marily for risk assessment and does not go into detail 33

regarding the related disciplines of threat and vulnerability assessments. Nevertheless, it 34

is important to understand these specialized tools for evaluating the susceptibility of 35

systems and faciliti es to potential threats , such as adversarial actions (e.g., vandalism, 36

insider sabotage, or terrorist attack ), natural disasters, and other emergencies. Therefore, 37

threat and vulnerability assessments are described briefly below. 38

39

Similar to a risk assess ment, threat and vulnerability assessments identify threats 40

and characterize the nature, probability, and magnitude of adverse effects. However, 41

vulnerability assessments not only consider the risk to the infrastructure itself, but also 42

consequences to th e surrounding community and environment, such as economic losses 43

and human health impacts from consuming contaminated food. Vulnerability 44

assessments also usually i dentify corrective actions that can reduce the risk or lessen the 45

severity of potential con sequences . Threat and vulnerability assessments are often used 46Page 24 Microbia l Risk Assessment Guideline

as tools to prioritize which threats or vulnerable operations need to be addressed first. 1

They are not actually a ―risk assessment‖ per se, although sometimes the results can be 2

similar to th ose produced in a classic microbial risk assessment, and the results can help 3

to inform risk management decisions. 4

5

Since the anthrax attacks of 2001, there has been growing awareness of the 6

potential for the deliberate use by terrorists of biological agen ts that pose a threat to the 7

health and welfare of the exposed sub population and to the economy. It may be 8

necessary to assess the risks associated with intentional contamination of the food or 9

water supply with biological agents, or release of biological agents as an aerosol into 10

highly populated indoor or outdoor public areas. 11

12

Threats and vulnerabilities can be characterized sequentially or as part of a single 13

assessment. Initially, the nature and probability of potential threats are determined. 14

Threa t assessments of intentional contamination include information on the availability 15

and ease of production of agents, as well as the sophistication and capabilities of the 16

likely perpetrator. Because of a lack of data and the typically random nature of ter rorist 17

events, the probability of threats can usually not be quantified statistically. 18

19

A vulnerability assessment helps you consider the potential for impact of 20

significant loss of operational and/or functional capability from a deliberate attack or an 21

an ticipated natural disaster, as well as the vulnerability of the facility/location itself to the 22

identified threats. For example, projected impact of loss can be viewed as the degree to 23

which the operation and critical infrastructure of a metropolitan drin king water 24

distribution system may be impaired by a successful attack involving a biological threat 25

or a natural disaster resulting in extensive biological contamination of primary source 26

waters feeding the distribution system. Consequences might also inc lude adverse health 27

impacts to customers of the water system and economic losses to the community. 28

29

Each scenario is unique and represents a different combination of factors affecting 30

the probability and the consequences of an intentional release of a biol ogical threat agent. 31

The health

- related factors might include the toxicity of the agent; the concentration of the 32

agent; fate of the agent in during processing, storage, distribution, and preparation of a 33

food product; quantity of contaminated food or wat er consumed; contagiousness; and the 34

availability and effectiveness of countermeasures, such as recalls and medical treatment. 35

The economic and public health impacts from a biological attack could be devastating 36

and catastrophic. Contaminated facilities may be out of commission for extended periods 37

of time for decontamination, and may never be able to return to operation or profitability. 38

The public may also lose confidence in the safety of the food and water supply. 39

40

A n effective v ulnerability assessmen t provid es a prioritized plan for mitigation 41

measures, such as security upgrades, modifications of operational procedures, and/or 42

policy changes , and provides a framework for develop ing risk reduction options and 43

associated costs. 44

45Microbia l Risk Assessment Guideline Page 25

Draft

– Do not cite, circulate, or quote Example Products of

Planning and Scoping

Statement of Concern

Statement of Purpose and Objectives

Background Section

Scope

Scenarios

Literature Review

Data Inventory

Tools and Methods Inventory

Risk management questions or Char ge

Risk Profile

Conceptual Model

Value

- of

- information analysis

Communication Plan

Analysis Plan

Work Plan

Data quality objectives A preemptive targeting prioritization tool you can use for a vulnerability 1

assessment is the CARVER plus Shock method (FDA, 2007), which has been adapted for 2

use in the food sector. This tool can be used to assess the vulnerabilities within a system 3

or infrastructure to an att ack. It allows you to think like an attacker by identifying the 4

most attractive targets for attack. By conducting such a vulnerability assessment and 5

determining the most vulnerable points in your infrastructure, you can then focus your 6

resources on prot ecting your most vulnerable points. 7

8

CARVER is an acronym for the following six attributes used to evaluate the 9

attractiveness of a target for attack (FDA, 2007): 10

11

a) C riticality

– measure of public health and economic impacts of an attack 12

13

b) A ccessibility

– ab ility to physically access and egress from target 14

15

c) R ecuperability

– ability of system to recover from an attack 16

17

d) V ulnerability

– ease of accomplishing attack 18

19

e) E ffect

– amount of direct loss from an attack as measured by loss in production 20

21

f) R ecognizability

– ease of identifying target 22

23

In addition, the modified CARVER tool evaluates a seventh attribute, the 24

combined health, economic, and psychological impacts of an attack, also called the 25

Shock attributes of a target (FDA, 2007). 26

27

2. 6 What is Discussed Duri ng Planning 28

and Scoping and What Products 29

Emerg e ? 30

31

Principal outputs from planning and scoping 32

can include various products that are 33

appropriate to the manageme nt objectives 34

and the plan for analysis of the risk. Not all 35

of these examples are necessary for every 36

occasion and certain ones will be more 37

appropriate for your particular problem. 38

Further, some outputs can be contained in 39

other products (e.g., t he ris k profile 40

document can contain many of the listed 41

produ c ts ). You need to discuss what outputs 42

will be generated during the planning and 43

scoping discussions. In general, many of 44

these products are good candidates for peer 45

review. Peer review early in the risk 46Page 26 Microbia l Risk Assessment Guideline

assessment process can provide timely insights, corrections to assumptions, and 1

directions on proper ways to proceed during the risk assessment. 2

3

During planning and scoping, participants can engage in a dialogue to answer the 4

following questions and commit the outcome of those discussions to paper to facilitate 5

mutual understanding (paper products underlined) : 6

7

a) What is the motivati on for the risk assessment? Drafting a Statement of 8

Concern is a good way to reach a common understanding of what broad issue the 9

risk assessment will address. Describe in simple terms what hazard is being 10

addressed and how it is thought to relate to human health for an exposure 11

scenario. Include any other driving factors for the risk assessment, such as a food 12

safety is sue s , regulatory requirements, public concern, or new scientific findings. 13

b) What are the management goals, issues, questions, and policies that need to 14

be addressed? A Statement of Purpose and Objectives is a concise paragraph 15

that addresses the managemen t goals. The management questions are framed as 16

questions and are written down. The management questions should be designed 17

so that if answered, the risk manager has the information needed to inform the 18

decisions that need to be made. 19

c) What is the conte xt of the risk assessment? Risk assessments are done in 20

historical and social contexts. It is helpful to summarize in a Background S ection 21

any previous risk assessments that addressed the same or similar hazards. In 22

particular, if your agency has conduc ted a previous risk assessment on the hazard, 23

then you can summarize the relationship between the current and previous risk 24

assessment. The context of a risk assessment may include new mandates, 25

regulatory requirements, policy de velopments, technical adva ncements, risk 26

assessment method and tool advancements, and new or enhanced data sets. 27

d) What is the scope and coverage of the risk assessment? The Scope outlines the 28

scenarios the risk assessment will cover. Answering the scoping questions below 29

can help ensure that you have the information regarding scope that is necessary to 30

conduct the MRA. 31

1) Which infectious disease hazard is being addressed (pathogen strain[s]

, or 32

indicator[s] , taxon [genus, species, strain/biovar] )? Define the hazard. 33

2) Which human sub p opulations will be included in the risk assessment (e.g., 34

g eneral population, life stages , or geographically defined sub populations)? 35

Describe which sub populations are explicitly included in the risk 36

assessment model, which will be accounted for implicitl y, and which 37

sub populations may be excluded by the risk assessment model (e.g., most 38

extreme behaviors). 39

3) What health outcomes or endpoints are addressed by the risk assessment, 40

and how is the health outcome measured? Clearly defining the health 41Microbia l Risk Assessment Guideline Page 27

Draft

– Do not cite, circulate, or quote endpoint i s important for transparency and also focuses the scope of the 1

risk assessment (e.g., infection, disease symptom/s, mortality). 2

4) What unit and routes of exposure are relevant and why? Determine the 3

time

- span of exposure relevant to the decision. 4

5) For risk a ssessments designed to derive nominally or presumptive ―safe‖ 5

levels of microorganisms (i.e. levels below a threshold of regulatory 6

concern) , what level of protection will be provided, and what is the 7

technical or policy justification for that level? Tran sparency in public 8

health objectives is important. 9

6) What specific exposure scenarios should be modeled? List specific 10

scenarios the risk managers would like to model (varying the inputs), 11

including desired spatial and temporal features. 12

e) What type of risk as sessment is needed to address the risk management 13

question(s)? Section 2.5.2 describes different types of risk assessments, 14

including quantitative , qualitative, risk ranking, product pathway, risk

- risk, 15

geographic

, and systems

- level

. 16

f) What is the state of the current knowledge? The planning and scoping can 17

include an overview of current knowledge and can be used to outline the topics 18

that will be reviewed in more depth in the rest of the risk assessment. A 19

Literature Review will likely help with understa nding the current state of the 20

science. 21

g) What and where are the available data? In addition to identifying the available 22

data, a Data Inventory can address data relevance, how the data will be used, data 23

accessibility considerations, and initial data quali ty evaluations. A n i nventory 24

may also provide notice to the public of the data currently available to the agency 25

in a call for data. See Section 2.6. 6 for further discussion of data quality. 26

h) How do I know what questions the risk assessment needs to answer ? The 27

Risk management Q uestions or Charge are usually written down and discussed 28

iteratively between the risk assessment team and risk managers so there is a 29

common understanding of the questions

. 30

i) What are the information/data needs of other members of th e “team ? ” The 31

risk assessment may be part of a larger project, such as an economic analysis. 32

There may be economic, social, or legal analyses that need to be coordinated with 33

the risk assessment. 34

j) How will you model the risk ? A Tools and Methods Invento ry should include 35

statistical methods for estimating model inputs and tools for addressing 36

uncertainty and variability and should make an initial determination of which 37

methods and tools are likely to be most useful. 38Page 28 Microbia l Risk Assessment Guideline

k) What are possible risk assessment or ri sk management options? Particularly 1

for risk assessments that are needed to evaluate intervention strategies or needed 2

to support regulatory determinations , different options should be presented as 3

different scenarios for the risk assessment. Those Scena rios can be clearly stated 4

during problem formulation and may evolve during risk assessment iterations. 5

l) What are the logistical considerations for conducting the risk assessment? 6

Logistical considerations can go into the A nalysis P lan (see Section 2.6.4) or a 7

W ork P lan document. These include: 8

1) r esources available to do the assessment, including funding and staff time ; 9

2) p articipants in the process and their roles ; 10

3) p lans for coordinating across offices, with other agencies and with 11

stakeholders ; and 12

4) s chedulin g (e.g., milestones, deliverable due dates, quality audits, 13

meetings), including provisions for timely and adequate internal, 14

independent external peer review , a nd if required, interagency review as 15

per EO 12866

. 16

m ) How will planning activities and results be communicated to senior managers 17

and to the public? Chapter 8 discusses risk communication within the context of 18

risk assessment. 19

n) What are the legal considerations and constraints that may shape the 20

ultimate decision and supporting risk assessment? The technical office within 21

the agency that is conducting the risk assessment can engage the agency‘s legal 22

department, according to normal practices within that agency. Legal context can 23

be discussed in the Background Section

. 24

o) How and at what iteratio ns will the risk assessment be peer reviewed? OMB 25

has published Information Quality Bulletin for Peer Review , which provides 26

general peer review guidance and sets minimal expectations for the review of 27

scientific information (OMB, 2004). Most agencies ha ve agency specific peer 28

review guidance that complies with the OMB guidance. You should follow your 29

agency‘s peer review guidance. For example, EPA‘s Peer Review Handbook 30

(EPA, 2000d, 2006c) provides guidance on selection of peer reviewers that 31

includes w here to find peer reviewers, what mix of expertise may be important, 32

representing diversity of disciplines, and limiting conflicts of interest. According 33

to the National Committee on Radiation Programs (NCRP, 1996), an expert has 34

the following characteris tics: 35

36

1 ) training and experience in the subject area resulting in superior knowledge 37

in the field ; 38

39

2 ) access to relevant information ; 40Microbia l Risk Assessment Guideline Page 29

Draft

– Do not cite, circulate, or quote 1

3 ) an ability to process and effectively use the information ; and 2

3

4 ) is recognized by his or her peers or those conduc ting the study as qualified 4

to provide judgments about assumptions, models, and model parameters at 5

the level of detail required

. 6

7

In summary, the planning and scoping discussion may include a preliminary 8

characterization of exposure and effects, as well a s examination of scientific data and 9

data needs, policy and regulatory issues, and scenario

- specific factors to define the 10

feasibility, scope, and objectives for the risk assessment. The level of detail and the 11

information that will be needed to complete the assessment also are determined. Just as 12

important, planning and scoping helps set the boundaries of the problem(s) addressed and 13

the scope of the MRA. 14

15

For perspective, risk managers (decision makers) naturally desire more 16

information, less uncertaint y, and more in depth interpretation, when the impact of their 17

decisions increases. They also want to know the financial and social implications of 18

possible decisions. The risk assessment may not be the appro p riate support analysis to 19

address these issues

, but if done well, risk assessment can be a critical input. These are 20

aspects that may be discussed during planning and scoping. 21

22

2. 6 .1 What are Risk Management Questions and What is the Charge? 23

24

A brief description of management questions is presented in Section 2.4. 25

Generating specific risk management questions for a risk assessment helps formulate a 26

clear, focused charge that identifies the technical and scientific issues on which you need 27

to address and suggestions for conducting the risk assessment. Formulating these 28

questions usually requires significant interaction between risk assessors and risk 29

managers, as well as dialogue with appropriate other parties (e.g. , those with relevant 30

information about the potential hazard). You want to be sure that the questions you 31

generate will focus the risk assessment to provide the appropriate analyses that can 32

properly inform the risk management decision at a level of detai l appropriate for the 33

issue . The resulting risk assessment can be designed to address and answer as best as 34

possible the risk management questions posed. 35

36

The charge focuses the assessment by presenting specific questions and concerns 37

surrounding such is sues as the comprehensiveness of the data, information, and literature, 38

the soundness of the methods proposed, the scientific support for the assumptions that 39

may be employed, and the sensitivity of the results to possible alternative assumptions. 40

As a ge neral rule, time is well

- spent preparing a good set of questions or a charge, as they 41

are crucial for an effective risk assessment and ultimate decision. In this context the 42

charge is the set of questions and does not imply a formal charge. 43

44

45Page 30 Microbia l Risk Assessment Guideline

2. 6

. 2 What i s a Risk Profile? 1

2

Codex defines a risk profile for food safety as: a description of a food safety 3

problem and its context that presents in a concise form, the current state of knowledge 4

related to a food s afety issue, describes potential microbiological risk management 5

options that have been identified to date, when any, and the food safety policy context 6

that will influe nce further possible actions... Consideration of the information given in 7

the risk profil e may result in a range of initial decisions, such as commissioning a 8

microbiological risk assessment, gathering more information or developing risk 9

knowledge at the level of the risk manager, implementing an immediate and/or temporary 10

decision (Codex Alim entarius Commission, 2007). 2 A typical risk profile includes a 11

description of the situation, product

, or commodity involved; information on pathways by 12

which consumers are exposed to the microorganism ; possible risks associated with that 13

exposure; consume r perceptions of the risks; and the distribution of possible risks among 14

different segments of the population. For a list of information that Codex recommends 15

including in a risk profile see Codex (2007). 16

17

A risk profile assists in identifying the risk m anagement questions that need to be 18

addressed . The risk profile should be clearly and thoroughly documented, so that risk 19

managers can use it to decide on further action in relation to a specific health issue. If 20

links are made between risk profiles for other risk assessments , risk profiles can provide 21

the basis for qualitative ranking of problems for subsequent risk management. 22

23

Notably, r isk profiles can also be used as decision tools that do not lead to risk 24

assessment. For examples see the New Zeal and Food Safety Authority. 3 25

26

2. 6.3 What is a Conceptual Model? 27

28

A conceptual model is a written or visual representation you construct of 29

predicted relationships between the hazard and exposed sub popula tions. 4 It is based on 30

your problem formulation and working hypotheses, supported by preliminary data and 31

information, and used to organize the conduct of a MRA. 32

33

The conceptual model depicts the movement of the hazardous agent to the host

. 34

Other tiers o f conceptual models may identify variables and data needed to conduct the 35

MRA

. A conceptual model (e.g., a source

- pathway

- receptor model; ―farm

- to

- fork‖ 36

model) can be developed early in the planning and scoping process

, to the level necessary 37

to address t he risk assessment‘s purpose. For example, in some cases , a pathogen may be 38

available in multiple medi a and cause different diseases depending on the route of 39

2 ―Risk profile‖ is sometimes used to refer to summary information at the end of each chapter in

frameworks based on the ecological risk assessment framework.

3 http:/ /www.foodsafety.govt.nz/science/risk

- profiles/

4 The conceptual model in planning and scoping differs from conceptual models that are used to map how

parameters are related in modeling software. The term conceptual model is used in both contexts in this

G uideline.Microbia l Risk Assessment Guideline Page 31

Draft

– Do not cite, circulate, or quote exposure . In those cases, during planning and scoping, you can clearly identify the 1

medi um , pat hway of exposure, and route of exposure to be assessed. 2

3

Developing a sound and useful conceptual model may require several iterations. 4

With the conceptual model, you can describe or visualize the relationships among the 5

assessment and measurement endpoin ts, the data required, and the methodologies that 6

will be used to analyze the data. An overall high

- level conceptual model as well as more 7

detailed conceptual models that cover just dose

- response or exposure asse ssment 8

components may be useful (EPA, 1998a )

. 9

10

2. 6

. 4 What is an Analysis Plan? 11

12

The analysis plan lays out the approach to be taken by the risk assessment team. 13

It shows how data sources and information will be used and integrated in the assessme nt 14

and how measurement endpoints (e.g., fecal shedding ) and uncertainties are related to the 15

assessment endpoints (e.g., morbidity and mortality) . As a product of planning and 16

scoping, the analysis plan can act as a bridge to the risk assessment. The ana lysis plan is 17

the implementation strategy for performing the risk assessment and addressing the 18

decision needs. It documents the agreements made during the planning and scoping 19

process and provides details on how the risk assessment will proceed. This pr ovides 20

transparency to the whole process. In addition, the analysis plan provides measures 21

against which the final risk assessment and its risk characterization can be evaluated. As 22

the risk assessment proceeds, the analysis plan may need to be revised t o ensure that the 23

risk assessment still meets the decision needs (EPA, 1998a)

. If a separate work plan for 24

logistics is not developed, then staffing, scheduling, and resource details can also go in 25

the analysis plan. 26

27

2.6.5 How are Data Gaps Identified an d Addressed in the Context of 28

Planning and Scoping? 29

30

Subsequent chapters in this Guideline address many of the tools used to address 31

data gaps. I ncomplete information and data gaps are a significant challenge throughout 32

the risk assessment discipline. Much of the ―art‖ in risk assessment involves the 33

judgments regarding incomplete data and data gaps. In addition to missing data, there are 34

commonl y questions about the degree to which available data are representative of the 35

actual conditions being assessed. 36

37

The extent to which a data gap exists is ultimately a matter of scientific judgment 38

within the context of what is an acceptable level of conf idence. Different assessors and 39

managers may have different comfort levels for making decisions based on the same data. 40

In some cases there may be differing options about how representative the data are and 41

whether the data adequately fit in the risk asse ssment scenario. The quality of existing 42

data is also considered when determining if a data gap exists (Section 2.6.6). To reduce 43

the heterogeneity of comfort levels, many different systematic schemes for evaluating 44

data quality, completeness, and applic ability have been developed. Statistical approaches 45

are standard for evaluating data quantity, but still require a judgment about the 46Page 32 Microbia l Risk Assessment Guideline

appropriate confidence level for decision making . If the risk assessors and risk managers 1

on your team cannot agree on w hat constitutes a significant data gap, the team may need 2

to take a step back and first agree on criteria for evaluating data. With some data gaps, 3

risk managers look to risk assessors to tell them whether the data are sufficient, while risk 4

assessors may claim that a policy decision needs to be made by the risk managers 5

regarding setting the threshold for sufficiency. These types of situations can stall a risk 6

assessment, but ultimately the group has to reach agreement on the judgment call or 7

policy deci sion. If the existence of a data gap is not obvious and agreed upon easily, the 8

evidence used to support the identification of the data gap should be clearly documented. 9

10

Stakeholders may have strong opinions about data gaps. Some may demand a 11

decision in the absence of data, while others may interpret the goal of science

- based 12

decision making to mean that more complete data must be available to make a decision. 13

Robust planning and scoping should be able to predict which data gaps have the potential 14

to cause the most debate. 15

16

Every parameter in the risk assessment will have some level of incomplete 17

information . Ranking the importance of the data gaps can help you focus resources on 18

the most critical data gaps that, if filled, could influence the risk assessment results the 19

most. Whether a data gap ―matters‖ to the risk assessment results can be evaluated by 20

conducting sensitivity analysis (Sections 5.3.3 and 6.7) or value

- of

- information (VOI) 21

analysis (Section 2.6. 7 ). Once a data gap is identified an d determined to be important it 22

can be a matter of scientific judgment or a policy decision that determines how the data 23

gap will be addressed in the risk assessment. You can fill the information needs in the 24

near term using existing data, in the midterm by conducting tests with currently available 25

test methods to provide data on the topic of interest, or over the long term to develop 26

better, more realistic understandings of exposure and effects and to construct more 27

realistic test methods to evaluate path ogens of concern. In cases where an aspect of risk 28

is likely to be important but insufficient data are available, highlight the deficiency or use 29

judgment or assumed values to approximate the missing data (see Section 6.4 for further 30

discussion). Such ju dgments and approximations should be clearly described and the 31

implications explained in the risk characterization. 32

33

OSTP states in their scientific integrity memo that, ―The accurate presentation of 34

scientific and technological information is critical to informed decision making by the 35

public and policymakers. Agencies should communicate scientific and technological 36

findings by including a clear explication of underlying assumptions; accurate 37

contextualization of uncertainties; and a description of the pro babilities associated with 38

both optimistic and pessimistic projections, including best

- case and worst

- c ase scenarios 39

where appropriate ‖ ( OSTP , 2010)

. 40

41

Depending upon the circumstances, the utility of a risk assessment may be 42

compromised if important policy decisions are put on hold while waiting for more 43

research results. Risk assessors and risk manage rs need to balance the need to obtain 44

more data/information against the need to make a timely decision. The data gaps 45Microbia l Risk Assessment Guideline Page 33

Draft

– Do not cite, circulate, or quote identified in planning and scoping may be very useful to establish a research program 1

and/or agenda to address current data gaps. 2

3

Expert opinion or judgment is a common source of information used in exposure 4

assessments. If no other empiric evidence is available, expert judgment may offer th e 5

best available science to inform a model. Alternatively, when data are completely absent 6

and the availability of expert opinion or judgment is questionable, it is possible to avoid 7

the need for such data by model simpl ification (Cox, 2006; Vose, 2008 ). Such an 8

approach is particularly worthwhile when empiric evidence is available to inform the 9

probability distributions of process outputs subsequent to the process that is missing data. 10

Such  ̳downstream‘ data (i.e., between the missing element and the ul timate exposure 11

distribution) actually reflect the likelihood of microbe levels given the processes 12

(modeled or missing) that occurred prior to the process. Therefore, processes for which 13

data are missing may effectively be skipped over if data are availa ble downstream. 14

15

Methods for eliciting expert judgments have been suggested (Kaplan, 2000). 16

Techniques for resolving conflicting opinions among experts focus on having experts cite 17

the experiences that inform them. In general, a diverse group of expert s is preferred 18

when eliciting input to the exposure assessment. 19

20

Scrutiny and analysis of large amounts of complex data is another use of technical 21

experts in exposure assessments. Convening an expert panel to review and assess 22

available evidence about a model‘s variables and parameters is often an effective 23

technique for endowing the exposure assessment with credible depictions of inputs and 24

their attendant uncertainties (ECSCC, 2003). 25

26

Ouchi (2004) and Morgan and Henrion (1990) provide summaries of metho ds and 27

citations for primary literature in the field of expert elicitation. Some of the methods 28

summarized by Ouchi (2004) and Morgan and Henrion (1990) include the following: 29

30

a) Behavioral approaches : 31

1) Face

- to

- face interaction 32

2) Delphi method 33

3) Nominal group te chnique 34

b) Mathematical approaches (for probabilistic risk analysis) : 35

1) Non

- Bayesian axiomatic models (opinion pools, performance

- based weight 36

model) 37

2) Bayesian models (additive error and multiplicative error models, stochastic 38

dependence) 39

3) Psychological scaling models (Thurstone model, Bradley

- Terry model, 40

negat ive exponential lifetime model) 41

It should be noted that Morgan and Henrion (1990) observed that ―because the 42

public decision maker must often informally factor in a number of other considerations, it 43Page 34 Microbia l Risk Assessment Guideline

is ra rely of great practical consequence that a more formal treatment in the combining or 1

weighting of alternative expert views is not possible.‖ 2

3

An example of how EPA has used expert judgment is the Office of Air Quality 4

Planning and Standards (OAQPS) report An Expert Judgment Assessment of the 5

Concentration

- Response Relationship Between PM 2.5 Exposure and Mortality (EPA, 6

2004a). Based on an NRC report recommendation, OAQPS used expert judgment to 7

develop probability distributions for key sources of uncertain ty regarding the mortality 8

effects of ambient fine (>2.5 microns) particulate matter (PM 2.5 ) exposure. EPA‘s 9

Environmental Benefits Mapping and Analysis program (BenMAP) 5 includes the 10

exposure

- response functions derived through expert judgment assessment as options for 11

risk modeling. 12

13

EPA‘s Science Policy Council‘s Draft Expert Elicitation Task Force findings 14

include (EPA 2009c): 15

16

a) Past experience with expert elicitation at EPA indicates that it can provide useful, 17

credible results. NAS has highlighted the se past efforts as exemplary and 18

recommended that EPA continue in the direction established by these precedents. 19

b) The use of expert elicitation is appropriate for some situations; but, not for others. 20

Factors favoring the use of expert elicitation include: inadequate information to 21

inform a decision, lack of scientific consensus, and the need to characterize 22

uncertainty. Factors favoring alternatives to expert elicitation include theoretical 23

and practical limitations. Typically, an expert elicitation requ ires a significant 24

investment of resources and time to provide credible results. 25

c) Expert elicitation can work well when a scientific problem has a body of 26

knowledge; but, lacks a consensus interpretation. For this case, expert beliefs 27

about the value and m eaning of data can provide valuable assessments and 28

insights. This may be the case for an emerging scientific challenge or one that 29

depends on uncertain future events. However, when a problem has abundant 30

relevant empirical data and relative consensus ex ists in the scientific community, 31

there is probably little need to conduct an expert elicitation

. At the other end of 32

the spectrum, if data are inadequate for the experts to develop judgments, an 33

expert elicitation may not be worthwhile. 34

d) Given that EPA us es other more familiar approaches to characterize uncertainty, 35

the application and acceptance of expert elicitation at EPA will likely grow with 36

experience. If early expert elicitation efforts are well designed and implemented, 37

this will promote the credi bility and endorsement of expert elicitation within the 38

Agency and by external stakeholders. 39

e) The nature of the regulatory process (i.e., legal, political, financial, technical, and 40

procedural considerations) will influence whether and how to conduct an exp ert 41

elicitation and how to communicate and use results. Within the regulatory 42

process, EPA can use expert elicitation to encourage transparency, credibility, 43

5 http://www.epa.gov/ttnecas1/benmodels.htmlMicrobia l Risk Assessment Guideline Page 35

Draft

– Do not cite, circulate, or quote objectivity (unbiased and balanced), rigor (control of heuristics and biases), and 1

relevance to t he problem of concern. 2

Hoffmann et al. (2007) used expert elicitation to attribute illnesses associated with 3

one of eleven major foodborne pathogens to the consumption of one of eleven categories 4

of food. They used responses from a large panel to create a nd analyze four uncertainty 5

measures: (1) agreement among experts; (2) expert agreement with prior estimate; (3) 6

mean individual expert uncertainty; and (4) variability in experts‘ individual uncertainty. 7

Hoffman and colleague‘s framework shows how these measures when viewed together 8

can provide greater insight into the state of knowledge available to support decisions, 9

than could individual measures. They used statistical analysis to assess the quality of 10

both expert judgment data and external data. Hoff mann and colleague‘s suggest that 11

analysis of multiple uncertainty measures is likely to be particularly useful to decision 12

makers when external validity checks that rely on conventional scientific methods or 13

further data collection is infeasible or costly

. 14

15

2.6.6 How do I Consider Information Quality Including Data Quality ? 16

17

Section 515 of the Treasury and General Government Appropriations Act for 18

Fiscal Year 2001 (Publ ic Law 106

- 554, also known as the ―Data Quality Act‖ or 19

―Information Quality Act‖) directed OMB to issue government

- wide guidelines that 20

―provide policy and procedural guidance to Federal agencies for ensuring and 21

maximizing the quality, objectivity, utili ty, and integrity of information (including 22

statistical information) disseminated by Federal agencies.‖ Federal a gencies responded 23

to the OMB guidelines (OMB, 2002) by developing Agency specific guidelines. For 24

example, EPA published Guidelines for Ensur ing and Maximizing the Quality, 25

Objectivity, Utility, and Integrity, of Information Disseminated by the Environmental 26

Protection Agency ( hereafter known as EPA Information Quality (IQ) Guidelines; EPA, 27

2002b). EPA‘s IQ Guidelines 6 include the following ad aptation of the quality principles 28

found in the Safe Drinking Water Act (SDWA) Amendments of 1996 (EPA, 2002b) : 29

30

a) The substance of the information is accurate, reliable and unbiased. This involves 31

the use of: 32

1) the best available science and supporting stud ies conducted in accordance 33

with sound and objective scientific practices, including, when available, 34

peer reviewed science and supporting studies; and 35

2) data collected by accepted methods or best available methods (if the 36

reliability of the method and the nature of the decision justify the use of 37

the data). 38

b) The presentation of information on human health, safety, or environmental risks, 39

consistent with the purpose of the information, is comprehensive, informative, 40

and understandable. In a document made ava ilable to the public, EPA specifies: 41

6 These principles should be adopted or adapted in all Federal agency IQ Guidelines for assessments related

to evaluations and public heath (OMB, 2002).Page 36 Microbia l Risk Assessment Guideline

1) each sub population addressed by any estimate of applicable human health 1

risk or each risk assessment endpoint, including sub populations if 2

applicable, addressed by any estimate of applicable ecological risk ; 3

2) the expecte d risk or central estimate of human health risk for the specific 4

sub populations affected or the e cological assessment endpoints , including 5

sub populations if applicable; 6

3) each appropriate upper

- bound or lower

- bound estimate of risk; 7

4) each significant uncert ainty identified in the process of the assessment of 8

risk and studies that would assist in resolving the uncertainty; and 9

5) peer

- reviewed studies known to the a dministrator that support, are directly 10

relevant to, or fail to support any estimate of risk and the methodology 11

used to reconcile inconsistencies in the scientific data. 12

Discussion of data quality is an important part of planning and scoping. You can 13

evaluate data quality within the context of your a gency‘s data quality guidelines and 14

work with mana gers to make decisions about what types of data should or should not be 15

included based on data quality and scope. Data quality requirements may differ 16

depending on the planned use of the risk assessment

. If data are excluded, it is always 17

important to no te the exclusion and reason for the exclusion. Methods for evaluating data 18

quality are important tools for producing a risk assessment that has both scientific value 19

and credibility with stakeholders. 20

21

In some cases development of Data Quality Objective s may be required by 22

Agency policy. Data Quality Objectives facilitate transparent documentation of the 23

justification of why data were included or excluded (IRAC, 2000)

. The following list 24

describes several important characteristics that may be helpful t o evaluate the usefulness 25

of data sets for risk assessment, such as submitter information, data source , methods, and 26

confidentiality (adapted from IRAC, 2000 ): 27

28

a) Ge neral information: Complete name and correspondence add ress of principal 29

investigator, purpo se of study, and a vailability of raw data 30

b) Source of data : Funding source/affiliation of principal in vestigator or data 31

collectors, w ho collected/produced the data, and f or numerical data, provide 32

numerator and denominator s tudy design: t ype of study, sam ple size, s ampling 33

frame/sample selectio n, and h ow sample relate s to the population (is the sample 34

from a particular country, region or producer?) 35

c) Data collection: Method of data collection/compilation, age of data, 36

c ountry/region of origin

, t ime frame f or collection (seaso nality), and c onditions of 37

collection (field versus laboratory data) 38

d) Microbial methods: Testing methods (which tests were run), s ensitivit y and 39

specificity of test(s), techniques used, precision of measurement, d efinition of 40

units bei n g used, species of animals used, if any, and s pecific organism tested or 41

studied 42Microbia l Risk Assessment Guideline Page 37

Draft

– Do not cite, circulate, or quote e) Evaluation of information: Consistency with regard to findings of other 1

researchers, publications that cite the data, peer review of the data

, i nv estigator's 2

evaluation of data, and i nvestigator's recommended limitations of data 3

f) Protections for sharing raw data: C onfidentiality for human subjects (blinded 4

data) 5

6

There are general criteria for evaluating data to decid e if it should be included in a 7

risk assessment. Basic questions to evaluate data include the following (adapted from 8

EPA, 1998 a ): 9

10

a) Are the study‘s objectives relevant to the risk assessment? The most relevant data 11

for risk assessment are those that focus on the (1) organism of interest; (2) 12

population at ris k; and (3) circumstances of exposure (e.g., vehicle, level, 13

timescale, and route). 14

b) Are the variables and conditions the study represents comparable with those 15

important for the risk assessment? 16

c) Is the study design adequate to meet its objectives? 17

d) Was the s tudy conducted properly? 18

e) Were there associations between observable data and the outcomes (health or 19

otherwise) of interest? 20

f) Are factors that could increase or attenuate risk (risk factors) controlled for in the 21

data? 22

g) How are variability and uncertainty tr eated in the study report? 23

h) Are the data sufficiently robust to be used to support a causal effect between 24

exposure and infection or illness? 25

i) Does the study meet agency requirements regarding ethics, such as having passed 26

internal review boards or complying with Agency regulations regarding research? 27

For example, EPA‘s Protections for Subjects in Human Research Rule 7 requires 28

that all pregnant women, all nursing women, and all children are excluded from 29

all studies involving intentional exposure that are int ended for submission under 30

pesticide laws. Additional information on the conduct and use of observational 31

studies in EPA‘s risk assessments are addressed in Scientific and Ethical 32

Approaches for Observational Exposure Studies (EPA, 2008). 33

EPA also has gen eral assessment factors, including soundness , applicability and 34

utility, clarity and completeness, uncertainty and variability, and evaluation and review, 35

7 http://www.epa.gov/oppfead1/guidance/human

- test.htmPage 38 Microbia l Risk Assessment Guideline

which are covered in more detail in EPA‘s Science Policy Council Assessment Factors 1

(EPA, 2003b). 2

3

Si nce data are never complete and are rarely collected specifically for risk 4

assessment ; many types of data are considered for inclus ion in risk assessment. Good 5

quality data include complete data sets , relevant data

, and peer

- reviewed data that are 6

consider ed high quality by experts in the field and agree with other data sets in terms of 7

comparison of methods and development of tests . Complete data sets would include 8

information on all the characteristics listed above . Relevant data may depend on the risk 9

q ues tion that is being addressed. Some c haracteristics of relevant data include age of 10

data, region or country of origin, purpose of study

, and species involved. 11

12

2. 6

. 7 What is a Value

- of

- Information Analysis? 13

14

During the planning and scoping discussions, a question you might ask is whether 15

to wait for additional research, or is there enough confidence in readily available 16

information to make a decision (Section 2.4.1 ). The aim of a value

- of

- information (VOI) 17

analysis for the decisi on maker will be in its ability to determine when no more 18

information about the risk of microbial pathogens is economically beneficial to making a 19

decision (Disney and Peters, 2003)

. For example, VOI is the amou nt a decision maker 20

would be willing to pay for information prior to making a decision. The maximum VOI is 21

for complete information. Consequently, the VOI analysis can contribute to the 22

identification of cost

- effective strategies for reducing a ri sk to ap propriate levels. VOI 23

analysis provides a way to quantify the value of actions taken to reduce the risk 24

associated with a decision (Hirshliefer and Riley, 1992). 25

26

Further more , decision makers could take a variety of actions to increase their 27

confidence in the estimates of the probability of human illness, before making a final 28

decision. VOI analysis could be used to identify optimal ―confidence

- increasing 29

strategies‖ for reducing uncertainty in quantitative risk analyses estimates of the 30

probability of an occurrence (Disney and Peters, 2003). 31

32

VOI analysis provides a set of methods for optimizing efforts and resources to 33

gather, to process, and to apply information to help decision

- makers achieve their 34

objectives (NRC, p ). NRC provides a schematic of th e application of VOI analysis to 35

assess the impacts of additional studies in a specific decision context. Information 36

opportunities that address uncertainties in the baseline model are considered with respect 37

to the changes they would have on the decision

- maker‘s preferred decision option and the 38

associated change in net benefits. The analysis may also consider any direct costs (for 39

example, financial) and indirect costs (for example, the health or economic impacts of 40

delayed decision

- making) associated w ith the information opportunity. The valuation of 41

information is ultimately driven by the decision

- maker‘s values with respect to the 42

distribution of risks and costs, including any costs associated with delayed decisions 43

(NRC, 2009). 44

45

46Microbia l Risk Assessment Guideline Page 39

Draft

– Do not cite, circulate, or quote 2. 6

. 8 What is a Comm unications Plan? 1

2

Risk communication (Chapter 8) can be initiated as soon as the risk assessment 3

process begins and can be incorporated throughout the process ; risk communication 4

s hould not be an aft erthought. During the planning and scoping discussions, the risk 5

communication specialists need to work with risk assessors, risk managers, and 6

appropriate stakeholders to develop a communication plan or strategy to be used in all 7

dealings with the public (essentially all stakeholders). In the plan you should consider : 8

9

a) C learly identify stakeholders and collaborators (e.g., in multiple partnering 10

projects)

. 11

12

b) S et forth specific communication goals (e.g., what will be your proposed 13

message(s))

. 14

15

c) E stablish w ho will be the audiences for whom you will communicate throughout 16

the risk assessment process

. The audience can range from technically 17

sophisticated risk assessors and knowledgeable risk managers to an educated lay 18

audience with limited knowledge of risk assessment. 19

20

d) P rovide for the development of the content of any communication tailored toward 21

your projected audiences

. 22

23

e) D evelop proposed outreach avenues to your audiences (e.g., fact sheets, press 24

releases, newsletters, notices, open meetings, briefings

, emails, websites )

. 25

26

You will most likely rely upon communication specialists, including your 27

agency‘s public affairs office, to get the message across. It is advis able that t hese 28

specialists be involved with the risk assessment effort from planning and scoping onward. 29

A principal spokesperson can be identified (possibly the risk assessor)

, and you can 30

decide which information channel(s) should be used. Your agency may have guidance 31

for risk communication plans or stakeholder involvement. For further i nformation 32

regarding risk communication from an assessor‘s point of view

, refer to Chapter 8. 33

34

2. 7 What Types of Decisions within Risk Assessment are Science Policy ? 35

36

This section discusses decisions made within the context of design and data usage 37

for risk assessment. It does not address the types of policy or regulatory decision making 38

that occurs after the results of the risk assessment have been considered. Scie nce policy 39

decisions are usually differentiated from scientific judgment calls. Whereas risk 40

assessors can make decisions based on scientific judgment, if a decision goes beyond 41

what would reasonably be considered firmly supported by science, then policy comes 42

into play. Once policy is involved, then risk managers need to become engaged in the 43

decision

- making. Failing to distinguish between policy decisions and scientific judgment 44

in a risk assessment is a serious threat to the scientific credibility of the assessment

. It is 45

important to note that: 46Page 40 Microbia l Risk Assessment Guideline

a) T he utilization of science policy in the risk assessment process is not meant to 1

―bury‖ or ―hide‖ risk management decisions within the risk assessment (i.e., any 2

science policy position or choice used in the r isk assessment process does not 3

direct the risk assessment itself toward a specific risk management decision)

. 4

b) To be transparent, policy choice s need to be stated explicitly in the risk 5

assessment. 6

c) A lthough science policy is utilized in the risk assessment process, it is important 7

to recognize that the policy positions themselves are developed outside the risk 8

assessment. 9

d) Scientific data should support s cience policy positions and risk assessors and risk 10

managers should ensure that the risk assessment proce eds in a way that best 11

serves the informational needs for subsequent decision(s) based on the risk 12

assessment

. 13

Science policy positions and choices are by necessity utilized during the risk 14

assessmen t process. You will likely use science policy positions in two major ways in 15

the risk assessment. 16

17

First, there are some basic, fundamental science policy positions that frame the 18

risk assessment process to ensure that the risk assessments produced are appropriate 19

for a particular decision. These scoping ―bou ndaries‖ for the risk assessment are 20

articulated during the planning and scoping process and ultimately explained clearly in 21

the risk characterization (i.e., what will be addressed in the risk assessment for 22

decision purposes, but also just as importantly, what will not be addressed and why 23

[ e.g., not p ertinent to the decision needed] ). These science policy positions not only 24

shape the risk assessment process, but are usually a factor in the decision making 25

process outside the risk assessment. 26

27

Second, the use of default assumptions in a risk assessment is a s cience policy 28

choice often invoked when there is a lack of data . These science policy choices are 29

more specific than the framing science policies mentioned above. Given the nature of 30

uncertainty and data gaps, default assumptions (sometimes simply called defaults) are 31

often used to address these uncertainties when data are unavailable or otherwise not 32

suitable for use . A default assumption is the option chosen on the basis o f a science 33

policy choice that appears to be the best choice in the absence of data to the contrary 34

(NRC, 1983). The NRC, in its review of risk assessment practices in Science and 35

Judgment in Risk Assessment (NRC, 1994), acknowledged that default assumpt ions are 36

needed to address uncertainty arising from a lack of data and other information

. The 37

report also stated that Agencies should have principles for choosing default options. 38

When pathogen

- specific data are unavailable (i.e., when there are data gaps) or 39

insufficient to esti mate parameters or resolve paradigms, a default can be used in order 40Microbia l Risk Assessment Guideline Page 41

Draft

– Do not cite, circulate, or quote to continue with the risk assessment

. T his is a science policy choice, generally agreed 1

upon during the planning and scoping discussions

, when data gaps are identified ( see 2

Section 2.4.1 for information on data gaps )

. During the risk assessment itself, a default 3

is used only when essential data are lacking

. Point estimates can also be considered 4

defaults, when the distribution of the parameter only ad d s unnecessary complexity given 5

the needs of the risk assessment . Fo r example, drinking water consumption is often 6

modeled probabilistically for MRAs with a median of 1L per day. The consumption 7

value of 2 L/day per person is often used for chemicals and represents the 90 th percentile 8

of the 1994 to 1996 and 1998 C ontinuing S urvey of F ood I ntake by I ndividuals 9

community drinking water consumption data

. As illustrated in this example, t he choices 10

you make need to be well within the range of plausible outcomes and often at specific 11

percenti les (for variability) within that range of observation. The use of 1L versus 12

2L/day is not related to differences in microbial versus chemical risk assessment. 13

The default assumptions are not pathogen

- specific per se , but are relevant to the 14

data gap in t he risk assessment. Defaults are based on published studies, empirical 15

observations, extrapolation from related observations, and/or scientific theory. A 16

representative list of the areas where assumptions are commonly made in MRAs is 17

presented in Appendi x A. 18

2. 8 What i s My Role as a Risk Assessor in Risk Assessment ? 19

20

People who perform the risk assessment, in whole or for the most part, are the risk 21

assessors. One often overlo oked responsibility as a risk assessor is to communicate your 22

key risk findings and conclusions and your confidence in them. Most of these 23

responsibilities are noted in other sections of this Guideline. Specific responsibilities 24

often include : 25

26

a) Collect, compile , and document appropriate data 27

b) Let the risk manager know whether the key data used for the assessment are 28

considered experimental, state

- of

- the art or generally accepted scientific 29

knowledge 30

c) Describe quantitative risk estimates in plain English; th e use of tables and 31

graphics may be helpful as a supplement 32

d) Describe the uncertainties inherent in the risk assessment and the default positions 33

used to address these uncertainties or gaps in the assessment 34

e) Refer the reader to an Agency risk assessment gui deline or other easily obtainable 35

reference that explains terminology (e.g., how a microbial criterion was 36

developed) 37

f) Determine what types and models are appropriate for the particular risk 38

assessment 39

g) Input data and models into software 40

h) R un modules 41Page 42 Microbia l Risk Assessment Guideline

i) Conduc t sensitivity analyses 1

j) Identify the subpopulations or systems addressed and provide an explanation for 2

the selection of these as the ―valued entities.‖ 3

k) Describe the level of confidence associated with the conclusions and assumptions 4

and defaults 5

l) Summarize and identify the key pieces of information critical to your evaluation 6

m) Put this risk assessment into a context with other similar risks that are available to 7

you and describe how the risk estimated for this pathogen or agent compares to 8

others regulated by your agency 9

n) Describe how the strengths and weaknesses of the assessment compare with other 10

assessments prepared in the past 11

o) Describe the rationale and bas i s for the conclusions drawn by those outside your 12

agency about this pathogen or agent 13

p) If their concl usions differ from yours, let the manager know whether theirs is a 14

reasonable alternative; can their conclusions reasonably be derived from the data 15

set 16

q) Inform the risk manager of the strengths and weaknesses of their evaluations 17

compared to yours 18

r) If you h ave developed specific assessments for one or more risk management 19

options, let the risk manager know what changes in risk would occur under these 20

various candidate risk management options 21

s) Highlight key or critical issues and, if needed, identify a hierarc hy of issues based 22

on importance to the assessment or those that could influence decisions makers. 23

t) Oversee and manage contractor support on the risk assessment, including review 24

of documentation and calcu lations for mathematical errors 25

u) Keep the decision ma ker informed of the status of your risk assessment and risk 26

characterization 27

v) Archive the risk assessment and associated materials in a manner consistent with 28

your organization‘s archiving procedures 29

30Microbia l Risk Assessment Guideline Page 43

Draft

– Do not cite, circulate, or quote 

## 3. HAZARD IDENTIFIC ATION AND HAZARD 1



## CHARACTERIZATION 2

3

Hazard identification and hazard characterization (HI/HC) are key components of 4

risk assessment. 8 In HI, the suspect hazard (i.e., the microorganism/adverse effect) is 5

identified a nd defined in the context of epidemiological, surveillance, clinical, microbial 6

(agent specific), and environmental (including meteorological and geographical) 7

information available to the risk assessor. The HC focuses on a particular 8

microorganism(s) and potent ial or known mechanisms of host

- pathogen interaction, 9

virulence, and pathogenicity

. As discussed in Section 1. 6

, the epidemiological triangle is 10

a useful framework for conceptualizing HC. Meteorological and geographic conditions 11

impact the persist ence and transmissibility of microbial agents in the environment and 12

influence the level of potential exposure. This chapter presents informa tion that is basic 13

to HI/HC. A list of questions that may be posed during HI/HC is presented in Appendix 14

B. 15

16

3.1 H ow do I Define the Hazard? 17

18

The term hazard broadly refers to the subject of an assessment. It can be 19

interpreted in a number of ways. It may be defined as the stressor agent capable of 20

causing an adve rse effect on the exposed individual or be defined as the adverse effect 21

itself. The subject of the risk assessment (hazard of interest) is a policy decision driven 22

by the existing statutes, regulations, or consistency with agency processes . You should 23

b e aware that the term hazard may refer to an agent that is causal or associated with 24

adverse outcome, the mechanism and metabolic products leading to the outcome, or 25

simply the subject of an assessment wherein the likelihood for adverse outcomes is being 26

e xplored. The adverse effects in humans that result from exposure to microbial agents or 27

metabolites under favorable host (healthy or susceptible due to certain life

- stages/pre

28

existing conditions) and environmental conditions may occur soon after exposure or arise 29

at a substantially later time (sequelae). This guideline focuses on pathogenic infectious 30

disease hazards. 31

32

Terms such as ―agent‖ and ―stressor‖ may sometimes be used synonymously with 33

hazard. 9 The microbial Thesaurus developed by EPA (EPA, 200 7b) diffe rentiates the 34

terms as follows: The term ―  ̳ stressor ‘ is used in ecological risk assessment and includes 35

but is not limited to the connotation that the adverse response can be the result of a lack 36

of something

– such as a habitat

– which would be called a  ̳ stressor ‘ . The term  ̳ agent ‘ 37

does not have this connotation.  ̳ Agent ‘ is used to denote a causative entity that actually 38

8 As noted previously, in the 1983 NRC Report, Hazard Characterization is the qualitative part of dose

response assessment. In this Guideline, because of the qualitative nature of Hazard Characterization it is

considered with Hazard Identification, which is also qualitative. Risk assessors can still organize the risk

assessment documentation under the headings provided by NRC or WHO.

9 Epidemiologists may use the term ―agent‖ sli ghtly differently. Agent

- a factor (e.g., a microorganism or

chemical substance) or form of energy whose presence, excessive presence, or in the case of deficiency

diseases, relative absence is essential for the occurrence of a disease or other adverse he alth outcome.

http://www.cdc.gov/excite/library/glossary.htmPage 44 Microbia l Risk Assessment Guideline

physically exists as part of the environment and can be used in either ecological or human 1

health risk assessment.  ̳ Hazard ‘ is used primarily in human health risk assessment, 2

although  ̳ hazards ‘ are not limited to  ̳ agents. ‘ For example, the number of days spent in 3

a hospital may be a hazard that correlates with risk of nosocomial infection. 4

5

In the context of MRA for frank pa thogens, the term hazard rep resents the 6

pathogen‘s potential to generally cause adverse effects in normally healthy humans, while 7

in the case of opportunistic microorganisms, the term hazard refers to potential to cause 8

adverse human outcomes under certain environmental and host conditions, most often 9

when the host is compromised. Thus, hazard may be used to denote the subject of the 10

assessment that needs further identification and characterization. 11

12

If the hazard is a pathogenic microorganism, then identi fication of the 13

microorganism is an important aspect of the hazard description. Microorganisms can be 14

categorized (defined) based on the methods used to detect them. Nucleic acid

- based 15

assays may result in a different categorization than culture

- based a ssays. As mentioned 16

previously, quasi

- species are particul arly hard to define (Section 1.5 ). It is important to 17

note that in some situations even small deoxyribonucleic acid ( DNA ) sequence changes 18

may elicit significantly different adverse outcomes in hu mans (Battista and Earl, 2004)

. 19

But in the case of ―zero tolerance‖ organisms, this distinction does not matter. For 20

examples of the nuances of microbial nomenclature, refer to Section 3.8. 21

22

In general, when performing assessments on individual strains or isolates, 23

identification rather than taxonomy or nomenclature becomes the issue. In this case, 24

identification refers to the placement of an isolate within an existing taxon, or 25

determining that it does not match any existing taxon, (i.e., proper label ing of the isolate), 26

as opposed to the creation of a new group or groups to which the isolate is related, that 27

share attributes with the isolate (i.e., classification of the isolate). The purpose of 28

identification is to ensure that you know which taxon or subtaxon you are evaluating. 29

30

Microbial i dentification is often anything but a trivial exercise. Unless an isolate 31

has been the subject of a taxonomic study, or is one of the cultures used to establish a 32

commercial identification method database that is kept current, it is often difficult to 33

ensure that an isolate belongs unequivocally to a specific taxon. In MRA, to develop 34

accurate exposure assessment and resulting risk characterization, you should carefully 35

evaluate the definition of the hazard and pa y close attention to how different data sets 36

used in the risk assessment define the hazard. 37

38

3.2 What are Hazard Identification and Hazard Characterization? 39

40

Hazard identif ication and hazard characterization provide a qualitative 41

examination of the hazard identified. The quantitative relationship between hazard and 42

effect is examined in the dose response assessment (see Chapter 4). Hazard identification 43

provides the framew ork for gathering relevant information to construct a realistic 44

scenario focusing on the likely microbial hazards present, so that they can be 45

appropriately considered for the MRA. In HI, information related to the epidemiological, 46Microbia l Risk Assessment Guideline Page 45

Draft

– Do not cite, circulate, or quote surveillance, clinical, and microbial aspects of the hazard is reviewed as a critical part of 1

the assessment. Hazard characterization helps you describe the mechanisms involved in 2

causing harm and the microorganism‘s ability or potential to cause harmful effects. 3

4

You should be aware that the extent of available data for HI, HC, and exposure 5

assessment vary greatly in MRAs. Coverage may vary depending on the type of the 6

assessment (e.g., qualitative, quantitative, retrospective, prospective) and agency policy 7

or convention. Yo u should consider the extent of coverage and potential overlaps in the 8

problem formulation step during planning and scoping. In this Guideline, HC and some 9

elements of exposure assessment are included in the HI/HC chapter. 10

11

If your Agency uses the WHO/F AO framework (Codex 1999) the preferred 12

chapter heading for topics discussed in this Guideline (Chapter 3) is H azard I dentification 13

and the qualitative descriptions related to dose

- response and quantitative aspects of dose

14

response are in a chapter titled Hazard Characterization. 15

16

3.3 What Hazard Characteristics Can I Consider? 17

18

There are several hazard characteristics that you can consider when assessing a 19

microorganism or its by

- products (adapted from USACHPPM, 2009 and EPA 2009 a ): 20

21

a) Infectivity

– the ability of a pathogen to enter, survive

, and 22

multiply (infect) in a host 23

b) Invasiveness

– the ability to degrade and migrate through 24

the extracellular matrix 25

c) Virulence

– the ability of the path ogen to defeat the host 26

defenses, increase the severity and longevity of the symptoms 27

d) Pathogenicity

– the ability to cause a disease state. It is 28

the cumulative effect of virulence and invasiveness (see the glossary for a 29

discussion of pathogenicity versu s virulence) 30

e) Host range

– which hosts a pathogen can infect. Some 31

pathogens have very specific host ranges; therefore the disease is limited to one 32

host. Other pathogens have wide host ranges, and they ca n cause disease in many 33

species 34

f) Horizontal gene tr ansfer

- the movement (transfer) of 35

genetic material (e.g., DNA) from one organism taxon to another that, with 36

maintenance and expression of that genetic material, may lead to antibiotic 37

resistance traits or acquisiti on of other virulence factors 38

g) Genetic d rift

– changes in the frequency of alleles 39

in a population due to random sampling 40

h) Replication

– the ability for a microorganism to multiply 41

within the environment or the host 42

i) Persistence

– the ability of the microorganism to survive in 43Page 46 Microbia l Risk Assessment Guideline

the environment or t he host 1

j) Transmissibility

– the ability of a microorganism to 2

survive, replicate, and pass through animate or inanimate matrices and stay 3

infective 4

k) Opportunistic Pathogens

– the ability of a usually 5

innocuous microorganism to cause an adverse health effect in a susceptible host 6

l) Taxonomy and Strain

– definition of the hazard 7

with respect to traditional biological classification

. Taxonomy and strain variation 8

have a potentially large impact on risk assessment. The difference in dose

9

response range between i solates (and strai ns) can be orders of magnitude . Some 10

strains may not be infective for humans. In addition, the ratio of different strains 11

i n the environment can fluctuate 12

m) Resistance to control or treatment processes

– the ability 13

of a pathogen to survi ve treatments, such as chlorination. If the risk assessment is 14

for a performance target then the treatment and control process es may be of 15

central importance 16

G enomics, proteomics, and metabonomics may all be important for hazard 17

characterization as well a s host characterization (EPA, 2006b). 18

19

3.4 How do Microbial Hazards Cause Adverse Outcomes? 20

21

The relationship between a host and a microorganism is complex with dynamic 22

interplay; the re are a number of mechanisms by which a pathogen can induce an illness. 23

In general, these include either direct invasion of the host cells and colonization of a 24

specific tissue or organ, causing necrosis or other direct damage or triggering host 25

response s that are self damaging , or through the production of toxic by

- products that can 26

elicit an adverse effect through toxicological modes of action. Note that toxins may be 27

produced by microbes in the environment or directly in the host. The conditions unde r 28

which microbes produce toxins and how host exposure occurs is important to consider. 29

For example hazardous algae blooms may result in oral or dermal exposure to toxins 30

produced by algae in the environment, whereas, pathogenic E. coli produces toxins 31

dur ing infection which can continue to cause damage to the host even after the microbes 32

die. 33

34

Among pathogenic organisms, there are several common patterns or themes in the 35

cascade of events that dictate the progression of disease process. The first essentia l step 36

in the establishment of a disease is the ability of the pathogen to adhere to a tissue. This 37

step, while prompted by the pathogen, is often the result of a host

- microorganism 38

interaction that is host specific. The second step is the invasion/penet ration of the host‘s 39

epithelium, whether the skin, lining of the lungs, or the lining of the gastrointestinal tract. 40

The pathogens are able to invade the host cell and establish a niche where they can 41

multiply. The success of a pathogen to initiate and c ause disease is limited by its ability 42

to counteract effectively the host defense mechanism and be able to multiply to a level 43

that elicits a symptomatic response . The ability of the microorganism to effectively 44Microbia l Risk Assessment Guideline Page 47

Draft

– Do not cite, circulate, or quote defeat or evade the host defense response d etermines the latency period, intensity, and 1

persistence of the disease state. 2

3

Modeling mechanisms of infection (e.g. how molecular and cellular host and 4

pathogen factors interact) may someday be applicable to MRA; however, currently the 5

science is not de veloped enough for the pathogens of concern and it is unclear how much 6

value this feature would add, given the large uncertainties in other areas of MRA. 7

8

3.4.1 What does Virulence and/or Pathogenicity Mean in the Context of 9

Causing an Adverse Outcome? 10

11

Pathogenic microorganisms have virulence factors with specific modes of action 12

for entry, colonization, and adverse health effects

. 10 The f irst step in assessing 13

pathogenicity is to collect the microbial evidence for the adverse health effects associated 14

with the agent of concern. To cause disease, pathogens must overcome various host 15

defense systems, and their ability to do this is indicati ve of the virulence of the 16

microorganism. Examples of some types of microbial virulence factors include: 17

18

a) Factors that help the microorganism persist in the environment 19

20

b) Factors that help the microorganism evade the host immune system 21

22

c) Expression of surfa ce proteins or polysaccharides that help bind the organism to a 23

specific site in the host 24

25

d) Production of toxins 26

27

In causing a disease, not only do the pathogenicity and virulence potential s of the 28

microbial agent play a role, but also the degree of suscept ibility of the host and the 29

influence of environmental factors on exposure influence the final outcome. 30

Understanding the interactions between a microbial pathogen, the host, and the 31

environment is key in determining the potential health impact a pathogen will have on an 32

individual (or a population). The classic epidemiological triangle (disease triad) 33

illustrates the inter

- relationship between the host, pathogen, and environment (See Figure 34

1.2). 35

36

3.5 What are the Mechanisms that May Lead to the Develo pment of New 37

Pathogens or Pathogens with New Traits? 38

39

In the microbial world, there exist mechanisms that consistently produce newer 40

strains of pathogens or to have existing pathogens acquire more virulent traits from other 41

microorganisms. Such mechanisms can result in the horizontal transfer of genes within 42

and between viral and bacterial strains. While horizontal transfer of g enes often results in 43

10 P athogenicity is the quality or state of being pathogenic, the potential ability to produce disease.

Virulence is the disease p roducing power of an organism, the degree of pathogenicity within a group or

species

.Page 48 Microbia l Risk Assessment Guideline

reductions in fitness, on some occasions the transfer results in more potent viruses and 1

other pathogens. Recent advances in whole genome nucleotide sequence analysis 2

demonstrate that viral, bacterial and protozoan pathogen evolution includes horizontal 3

gene transfer of virulence factors between different species and high taxa . Thus an 4

understanding of the role of horizontal gene transfer between different pathogens is 5

essential for the evaluation of the possible introduction of new microbial hazards. This 6

may be as a result of an unintentional or deliberate environmental release of natural or 7

genetically modified microorganisms. 11 8

9

Acquisition of new traits comes about by the transfer of genetic traits vertically or 10

horizontally amo ng microbial species. All living organisms have at least one natural 11

mechanism for genetic transfer. Techniques of biotechnology take advantage of these 12

mechanisms to precisely transfer desired characteristics or remove undesirable ones in 13

genetically mo dified bacteria. In prokaryotes, these mechanisms include: 1) conjugation, 14

through which portions of genetic materials are exchanged between two related cells in 15

physical contact; 2) transduction which occurs through infection by a virus intermediate, 16

a b acteriophage; and 3) transformation in which there is direct uptake and incorporation 17

of extracellular DNA. Facilitating these transfers are genes for mobilization of DNA 18

from one genomic compartment to another, as from a large replicon (chromosome) to a 19

smaller one (plasmid). These are often found in insertion elements and transposons. 20

21

It is commonly recognized that mobile genetic elements have contributed to rapid 22

changes in virulence potential by the acquisition of new traits that increase their survi val 23

and adaptation in human hosts and in adverse environmental conditions. When 24

mobilizing genomic elements (phages, plasmids, insertion elements, or transposons) 25

acquire such functional gene segments, selection can segregate these into self

26

transmissible units, called  ̳genomic islands‘. When specific traits or virulence factors 27

that contribute to pathogenicity are included within these units, they are called 28

pathogenicity islan ds (Knapp et al., 1986; Schmidt et al., 2004). The advent of whole 29

genome seq uencing and other advances in molecular biology has allowed development of 30

criteria for recognizing pathogenicity islands in microorganisms of interest (Guzman et 31

al., 2008; Yoon et al., 2007; Dobrindt et al., 2004). 12 In some cases, where the suspect 32

micr oorganism is known to be related to ones that have been sequenced, the use of 33

sequence analyses can be employed to look for components of pathogenicity islands or 34

virulence factors. 13 As indicated in section 3.4.1, bac terial pathogenicity determinants 35

are generally grouped as virulence factors o r mechanisms which includes antibiotic 36

resistance, pore

- forming toxins, superantigens (Schmidt et al., 2004) 14 and even quorum 37

sensing (Lerat and Moran, 2004). Thus the knowledge of mechanisms that have the 38

potential to result in microorganisms with new pathogenic traits may be of critical 39

importance in conducting certain types of risk assessments. 40

41

42

11 Genetically modified microorganisms are a potential topic for a future volume of this Guideline.

12 http://www.gem.re.kr/paidb/about_paidb.php

13 http://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.html ; http://www.genomesonline.org/ ;

http://www.tigr.org ; http://www.sanger.ac.uk/

14 http://www.gem.re.kr/paidb/about_paidb.phpMicrobia l Risk Assessment Guideline Page 49

Draft

– Do not cite, circulate, or quote 3.6 What are the Major Categories of Microorganisms? 1

2

The major microbial categories that cause adverse outcomes to humans include 3

bacteria, fungi, viruses, protozoan parasites, algae, and indeterminate types (Table 3.1). 4

Helminthes (tapeworms, roundworms) are also considered to be hazardous organism s

, 5

particularly if direct exposure to feces is possible

. Although helminthes are mul t i cellular 6

parasites and not microorganisms, they are sometimes considered in conjunction with 7

pathogens because infectious stages are too small to be easily detected by t he naked eye. 8

9

T here is a vast array of microorganisms and associated literature on pathogenic 10

genera, species, subspecies, strain and subtypes , which is outside the scope of this 11

document. There is vast literature available on taxonomic characterization of microbial 12

agents

. However, depending on the specific requirement of an assessment, it is 13

recommended that an assessor consult relevant literature and subject matter experts as 14

needed. Under some circumstances the hazard may not be identifiable however the 15

human health effects may be distinct. These are hazardous agents of indeterminate type 16

but may still be clinically well defined enough to facilitate risk assessment approaches. 17

18

Table 3.1 presents some of the major categories of microbial hazards in the 19

context of MRAs (Labbe and Garcia, 2001; Peter, 1998; Alexopoulos et al., 1996) . The 20

broad categorization of microbial organisms should help you understand how an agent in 21

a given category causes disease in humans. The placement of hazardous organism s into 22

broad categories is particularly important in retrospective assessments to narrow the 23

focus of investigation based on documented history for the category in question. 24

25

Batz et al

. ( 2004 ) constructed a comprehensive list of pathogens for the 26

Foodborn e Illness Risk Ranking Model (FIRRM) analytical software tool using data 27

generated by various federal agencies. This includes estimates of the incidence of 28

foodborne illness by CDC as reported by Mead et al. ( 1999 ) and cost

- of

- illness studies by 29

the USDA‘ s Economic Research Service, with additional data reported by FDA, USDA 30

FSIS, and Center for Science in the Public Interest. An additional list of foodborne 31

pathogenic organisms and toxins compiled by FDA commonly called Bad Bug Book may 32

also be useful (C FSAN, 2006). A review of waterborne pathogens is provided by Craun 33

et al

. (2010). 34

35

EPA ‘s Water Quality Criteria program, 15 which address es microbial 36

contamination of the nation's waters under the Safe Drinking Water Act (SDWA) and the 37

Clean Water Acts (CW A) , provides information on microbial methods, Health 38

Advisories, Regulatory Support and Criteria Documents. Health Advisories serve as 39

informal technical guidance to assist federal, state, and local officials responsible for 40

protecting public health when emergency spills or contamination situations occur. 41

Criteria documents and guidance for drinking water contaminants provide information so 42

preliminary decisions can be made as to whether the contaminant is a significant health 43

threat via drinking water exp osure and whether sufficient data exists to perform 44

quantitative risk assessments. 45

15 http://www.epa.gov/waterscience/criteria/humanhealth/microbial/Page 50 Microbia l Risk Assessment Guideline

1

EPA‘s Candidate Contaminant List of microbial organisms (CCL3) 16 is a list of 2

twelve microbial agents currently not subject to any regulation based on a contaminant's 3

pote ntial to occur in public water systems and the potential for public health concern . The 4

list of chemical contaminants includes cyanotoxins produced and released by 5

c yanobacteria ("blue

- green algae"). 6

7

16 http://www.epa.gov/ogwdw000/ccl/ccl3.html#microbialMicrobia l Risk Assessment Guideline Page 51

Draft

– Do not cite, circulate, or quote Table 3.1 Major Categories of Foodborne and Waterborn e Microorganisms 1

* The vehicle exposure/pathway may be important as the agent is indeterminate 2 Category Features

Examples Morphological Physiological Genetic Pathogenicity

Adaptation

M echanisms

Bacteria Francisella

tularensis

, Bruce lla suis

, Escherichia coli

O157:H7

, Salmonella

, Campylobacter

, Listeria

, Legionella

Cyanobacteria

, Vibrio S ingle

- celled

p rokaryotes Metabolically

diverse, invasive,

produce

intra/extracellular

toxins No nucleus,

double stranded

DNA, presence

of extra

chromosomal

DNA/Plasmids,

mutate

frequently,

horizontal gene

transfer

mechanisms Some species form

sp ore s to withstand

adverse conditions.

Mutation and gene

transfer, pathogenicity

islands and other

genetic

traits/mechanisms lead

to frequent strain

variation, acquisition of

enhanced virulence

traits and adaptation to

new environment s

, toxin production

Viruses N oroviruses,

A denoviruses

, Hepatitis A Acellular, most

are enve loped

with geometric

structures Metabolically

inactive,

obligatory

parasitic, host

dependent Single or double

stranded RNA or

DNA, mutate

rapidly in host Frequent genetic drift,

shift a nd other genetic

mechanisms may lead

to changes in antigenic

properties, host

survival/adaptation and

result in more virulent

variants/strains

Protozoa Toxoplasma

gondii,

Giardia spp.,

Cryptosporidium

hominis,

Cryptosporidium

parvum,

Naegleria spp. Si ngle celled

Eukaryotes of

the Protista

, display different

morphologic

structures and

stages of

infectivity Host dependent

parasites Nucleus present,

not known to

mutate as

frequently as

bacteria and

viruses Cyst and spore formed

to withstand adverse

cond itions. Relatively

stable genome,

however,

mutation and gene

transfer may lead to

strain variation,

enhanced virulence and

adaptation to new

environment

Fungi Aspergillus

fumigatus,

Penicillium,

Candida

, Aspergillus flavus Eukaryote

Mostly

multicellul ar and

filamentous,

pathogenic fungi

are mostly

unicellular (e.g.,

yeasts) Metabolically

diverse, invasive,

produce

mycotoxins Nucleus present,

sometimes

presence of extra

chromosomal

DNA/Plasmids Spores

Algae

Chlorophyta,

Rhodophyta

D inoflagellata Pfies teria

piscicid, ―red

tide‖

Gambierdiscus

toxicus

(Ciguatera) Single

- celled

photosynthetic

organisms, often

dinoflagellates,

Eukaryotes Metabolically

diverse highly

complex life

cycle, a few toxin

producing Nucleus present Three typical forms are

classif ied as amoeboid,

flagellated and encysted

varieties

Indeterminate

agent * C an vary,

unknown C an vary,

unknown C an vary C an vary,

unknown C an vary, unknownPage 52 Microbia l Risk Assessment Guideline

1

3. 7 What Methodologi cal Approaches can be Used to Identify and Quantify 2

Microorganisms? 3

4

You should be familiar with laboratory approaches for identifying and quantifying 5

the microorganism(s) of concern. For any datasets that required laboratory methods that 6

were used in risk assessment, carefully review issues related to sensitivity, specificity, 7

limit of detection, sampling method, and sample size. Extensive reviews of microbial 8

methods are availab le ( CFSAN , 2001; USDA 2008; AOAC International 2007). 9

10

Identifying an unknown microorganism is a two

- step process, requiring methods 11

to characterize the traits of an organism and approaches to interpret the characterization 12

dat a. Methods to generate characterization data range from traditional culture

- based 13

phenotypic and biochemical tests to the more recently developed molecular techniques. 14

Approaches can reflect the evolutionary inheritance of traits ( e.g., lineal decent), t he 15

intrinsic properties of the organism regardless of how they were acquired, or a 16

combination of both. 17

18

Methods used for identification and quantification are often related to similar ones 19

used for classification of microbes. However, the different purpo ses of classification and 20

identification require separate considerations, even when the same technology is utilized 21

for both. Under the controlled conditions of a classification study a particular 22

methodology may be exquisite in its ability to distinguish among selected related isolates, 23

but that same method may be only able to provide an approximate identification when 24

one encounters an isolate outside the lab without the benefit of closely related isolates 25

available for comparison. 26

27

While traditional cul turing methods for identification and quantification of 28

microbes are still the mainstay for fecal indicators , it is clear that this approach by itself 29

does not allow for complete evaluation of microbial organisms beyond the genus and 30

species level , and doe s not detect strains that may be active/infectious but non

- culturable

. 31

The classical culture

- based approach relies on culture of the organism in question, 32

isolation in pure culture, and a study of the morphological, biochemical, physiological 33

and other tr aits. However, not all microorganisms are culturable (see viable

- but

- not

34

cultu rable discussion in Section 3.8 ). 35

36

More sophisticated methods have the ability to discriminate subtypes and also 37

capture information on pathogenicity determinants of interest a t the genotypic and 38

phenotypic levels, either with isolated cultures or even in mixed enrichments. Some 39

molecular methods can be used even in the screening steps early on, to capture the 40

virulence potential of a specific microorganism or multiple microorg anisms (profiles) 41

without the isolation steps that are generally less sensitive and more time

- consuming. 42

43

44Microbia l Risk Assessment Guideline Page 53

Draft

– Do not cite, circulate, or quote 3. 8 Are there any Special Concerns Regarding Microbial Detection Methods? 1

2

Purpose of risk assessment and choice of the method(s) 3

4

Discrimination down to the smallest organizational level (i.e.

, strain) is not 5

necessarily the objective of all identifications done for risk assessments. You should 6

determine w hat information is critical for completing the assessment before pursuing a 7

specific level of identification. If the pathogenic potential is associated with all members 8

of some higher order taxa, then identification to that higher taxonomic level may be a ll 9

that is required for broad

- based assessments. For example, knowledge that an isolate is a 10

member of a species complex with many shared characteristics may be sufficient to 11

permit assessment of potential for pathogenic effects, such as the Mycobacterium avium 12

Complex ( MAC ) . However, if evaluation of a specific incident is the purpose of an 13

assessment, it is important to note that even the lowest levels of taxon (SNP variant) are 14

known to elicit significant adverse outcomes. For examples, an enterohemor rhagic type 15

of Escherichia coli known as serotype O157:H7, can cause serious hemolytic uremic 16

syndrome and even death. It is known that the degree of adverse outcomes seen with E. 17

coli O157:H7 infections vary distinctly among different clades (group of SN P subtypes) 18

(Manning et al., 2008). Thus, in retrospective assessments you should pay attention to 19

detailed information within a taxonomic subunit. 20

21

When doing assessments for broad categories of microorganisms, genus

- level 22

identifications may be all tha t is needed. Culturing techniques that involve phenotypic 23

analyses may suffice. However, for this level of identification, 16S rDNA analyses have 24

become commonplace and are usually deemed adequate for prokaryotes. Morphological 25

features are traditionall y used for fungi and protozoa, but biochemical and molecular 26

methods are beginning to be essential to avoid miss

- identifications. 27

28

Culture related issues 29

30

Many non

- spore

- forming bacteria exposed to environmental stress conditions may 31

decline in number and n ot be detectable by traditional culture based laboratory methods, 32

depe nding on the level of detection for each method and the number of replicate cultures 33

employed. To ensure that microorganisms in the viable

- but

- nonculturable (VBNC) state 34

are not missed , you should be aware of relevant methods o f direct identification such as 35

molecular methodologies 17 for detection and direct microscopy. For microorganisms 36

present at low levels, specific enrichment cultures can be employe d that allow growth of 37

very low p opulation numbers to levels high enough for traditional culture techniques. A 38

VNBC state has been described where metabolic characteristics are quantifiab le though 39

cells cannot be grown on traditional culture media, most notably in the genus Vibrio

, 40

under conditions when water temperatures dro p below 10°C (Smith and Oliver, 2006; 41

17 There are methods to isolate the DNA from environmental samples that do not require a pre

- e nrichment

culture, such as length heterogeneity

- polymerase chain reaction (LH

- PCR). Methods such as LH

- PCR

require as little as 10 ng of DNA in the PCR reaction to amplify 16S rRNA sequences (Bisson, et al.,

2007). In addition, PCR has been used to detect antibiotic resistance genes in bacteria found in deli

- meats

(Li and Wang, 2010).Page 54 Microbia l Risk Assessment Guideline

Fischer

- Le Saux et al., 2002 ; Rowman, 2004; Huq et al., 2000 ). Ot hers have shown that 1

under such stress conditions very small numbers of s urviving cells are able to grow to 2

higher levels using dead cell mate rials remaining in the stressed culture, therefore giving 3

the appearance that the cell state had changed from nonculturable back to culturable 4

(Bogosian et al., 1998). Molecular methods generally detect living and non

- living 5

pat hogens, unless specific methods have been developed that target aspects of their 6

growth viability (Sen and Ashbolt, 201 1). However, some important pathogens, such as 7

Norovirus, c annot be reliably grown in cell culture, so there is currently no way to chec k 8

on the infectivity of environmentally recovered virions of Norovirus. 9

10

Similarly in the case of parasites and viruses that are nonculturable and do not 11

replicate outside the host, serological or advanced molecular methods have been routinely 12

utilized in identification and reporting. Other methods of detection may also be needed 13

where pre

- formed metabolites/toxins may be present but the suspect pathogen cannot be 14

recovered, for example, emetic toxin for Bacillus cereus or endotoxins in aerosols. 15

16

Amplif

ication of organisms by growing in culture can also exert selective 17

pressure, which can skew identification of microbes from environmental or clinical 18

samples. You should be aware of any methods related considerations that might impact 19

your judgment of th e relevance or quality of a particular study. 20

21

Level of discriminating power 22

23

The two major subtyping approaches commonly used are based on our ability to 24

discriminate phenotypic or genotypic traits. The phenotypic approach includes 25

serotyping, phage ty ping, multilocus enzyme electrophoresis, and esterase typing. The 26

genetic subtyping approach encompasses pulse d field gel electrophoresis (PFGE), 27

ribotyping, PCR

- based subtyping techniques ( e.g., random amplification of polymorphic 28

DNA [ RAPD ]) , amplified fragment length polymorphism (AFLP), PCR

- restriction 29

fragment length polymorphism (PCR

- RFLP), repetitive element PCR (REP

- PCR), DNA 30

sequencing

- based subtyping techniques ( e.g., multilocus sequence typing [ MLST ] ) (Liu, 31

2006), multilocus genotype typing (MLG T), and single nucleotide polymorphisms 32

(SNPs). Some of these molecular based subtyping techniques provide not only powerful 33

discriminating capabilities to further identify a unit of potential hazard in question, but 34

also to relate microbial t raits to pub lic health outcomes and perhaps most importantly, 35

enables subtype

- based surveillance to detect outbreaks. 36

37

Depending on the pathogen/unit of hazard of interest, pay close attention to the 38

discriminating power of the methods used and level of details needed in risk assessment 39

based on the risk management needs. For example, there are about 3000 serotypes within 40

the species Salmonella enterica with differing levels of human pathogenicity. Subtyping 41

strategies include serotyping and further determination of the virulence traits important to 42

public health. For example, subtyping S. enterica serotype Typhimurium definitive type 43

104 ( DT104) involves identifying isolates through serotyping, followed by antimicrobial 44

susceptibility testing to identify the R

- type ACSSuT (ampicillin, chloramphenicol, 45

streptomycin, sulfonamides, and tetracycline) antibiotic resistance pattern. S. enterica 46Microbia l Risk Assessment Guideline Page 55

Draft

– Do not cite, circulate, or quote serotype T yphimurium isolates with R

- type ACSSuT are subsequently phage typed to 1

specifically identify DT104 (Akkina et al., 1999 ). 2

3

In the case of the bacterial human pathogen Listeria monocytogenes, which is 4

made up of a range of strains/genotypes with varying degree of pathogenicity, some 5

strains may be highly pathogenic and sometimes deadly; others may be relatively less 6

virulen t and cause little harm in the host. The ability to differentiate strains of L. 7

monocytogenes is particularly important for microbial hazard identification and for 8

tracking transmission of pathogenic strains within a given environment via food 9

processing environments. The purpose of subtyping is to link human infections that may 10

be related

, detecting unusual clusters of human disease, and determining the source of 11

exposure thorough epidemiological investigation, as well as finding and controlling the 12

sour ce of contamination. The application of molecular techniques has facilitated the 13

identification and characterization of major virulence

- associated genes and proteins in L. 14

monocytogenes . Various DNA fragment

- based typing methods have been used to 15

differe ntiate L. monocytogenes strains at the subspecies level to include epidemic clones, 16

genotypes, lineage types, and serotypes. 17

18

Differences in methodological approaches and choice of method(s) 19

20

Phenotypic methods include techniques that directly or indirec tly detect, measure, 21

or characterize features of a microorganism resulting from the observable expression of 22

its (total) genetic constitution. Phenotypic characteristics of bacteria include 23

morphological, physiological, and biochemical features. Methods for characterizing 24

phenotype require growth of the microorganism in pure culture under appropriate 25

conditions. Chemotaxonomic methods examine phenotype by using quantitative analysis 26

of the organism‘s chemical constituents. Genotypic methods directly comp are nucleic 27

acid sequences, rather than rely on gene expression. 28

29

Approaches to data interpretation can be determinative, numeric taxonomic, or 30

phylogenetic. Sometimes neither genotypic nor phenotypic methods alone suffice for 31

either classification or ide ntification of some bacteria, but it may be possible to combine 32

these methods using polyphasic taxonomy in which data from phenotypic, 33

chemotaxonomic (e.g., cell

- envelope lipids, electrophoresis patterns), and molecular 34

methods (e.g., ribosomal deoxyribonu cleic acid [ rDNA ]

, DNA gyrase, subunit B [ gyrB ] ) 35

are combined. When results from these methods all agree, you can usually rely on the 36

outcome. However, conclusive data may not be always available for a microorganism of 37

interest. 38

39

Other considerations for identification 40

41

The proper use of methodology needed to identify (classify) the subject of an 42

assessment depends on the level of detail required, whether prospective or retrospective, 43

and cannot be over emphasized. When the hazard units are whole taxa, y ou need to 44

consider the historical perspective and the current state of taxonomy and nomenclature 45

for that unit to ensure that relevant data and information are compiled. Scientific and 46Page 56 Microbia l Risk Assessment Guideline

technological advances make the field of microbial classification dyn amic in that the 1

taxonomic and other ways of classification are continually improving our understanding 2

of the unit of infectious structure (e.g., genus, species, subtypes, SNPs), their risk 3

potential, and mechanisms of pathogenicity. Phylogenetic analyse s of microorganisms 4

have resulted in frequent re

- assignment of microorganisms into different genera or 5

species, so synonyms for the microorganism may need to be tracked. Additionally, the 6

evolving nature of microorganisms enables them to acquire newer tra its for 7

pathogenicity, host range, specificity, adaptability, and survivability outside of and within 8

the host that add to the complexity of HI. 9

10

You should consider defining the unit of hazard in the context of the evolving 11

taxonomic information and risk management needs. For example, the term Burkholderia 12

cepacia had been applied both to a single species and groups of strains, termed 13

genomovars ( Mahenthiralingam , 2000; Vandamme et al., 1997, 2000) . These groups of 14

strains have subsequently acquired spec ies status, with independent names being 15

established. The latter have been called, and remain, the Burkholderia cepacia complex 16

(Bcc)

. Burkholderia cepacia is now construed as a single species within the Bcc, but 17

isolates of other species in the complex have, at earlier times, been called B. cepacia

, 18

with attendant literature using only that epithet. However, at times the unit of hazard 19

may not be identifiable as a structural taxonomic unit. You should become aware of such 20

issues to be able to refine th e scope of the unit of hazard in question. 21

22

When performing assessments with a hazard unit that is an individual strain or 23

isolate, identification rather than taxonomy or nomenclature becomes the issue. In this 24

case identification refers to the verificati on of the labeling of an isolate, as well as the 25

placement of an isolate within an existing taxon, or determining that it does not match 26

any existing taxon. The purpose of identification is to be sure that you know which 27

organism/unit of hazard applies to a data set. For example, if occurrence data are for a 28

set of isolates and dose

- response data are for only one of the isolates, then the uncertainty 29

this introduces needs to be discussed. 30

31

In rare situations, the unit of hazard (e.g., genus, species, su btypes) may not be 32

identifiable. However, defined host symptomatology may lead to an underlying suspect 33

agent (Soller et al., 2010) . Under such circumstances, the vehicle of transmission of the 34

suspect agent may be considered as the unit of hazard. Over all, the level of details in 35

microbial HI/HC depends on the risk assessment characteristics and risk management 36

needs (e.g., issues, goals). 37

38

3. 9 What Host Factors Can I Take into Consideration? 39

40

You can consider the following factors when evaluating potential health effects 41

due to exposure to a pathogen. Following each factor is a short description of how the 42

factor may influence the health outcome. There may be little or no data fo r these factors, 43

so their consideration may be limited to a qualitative discussion (adapted from 44

USACHPPM, 2009; EPA 2009 a )

. 45

46Microbia l Risk Assessment Guideline Page 57

Draft

– Do not cite, circulate, or quote a) Age/Life Stage

– L ife stage refers to a distinguishable time frame in an 1

individual's life characterized by unique and relatively stable behavioral and/or 2

physiological characteristics that are associated with development and growth. 3

Children and elderly are usually considered more susceptible due to immaturity or 4

other potential weaknesses in their immune systems. Behaviors that a ffect 5

pathogen exposure patterns may also be related to age. For example, children 6

may also experience greater exposure (therefore larger doses) due to their 7

behaviors (e.g., close

- proximity playing, hand

- to

- mouth tendencies). 8

9

b) Pregnancy

– Women who are pregnant and fetuses are generally considered to be 10

a sensitive and perhaps more susceptible life stage

. For example, Hepatitis E, 11

which is a self

- limiting disease for most people, can cause up to 20% mortality in 12

women in the third trimester of pregnancy (Jameel, 1999). The underlying reason 13

for increased susceptibility is due to the influence of pregnancy on the immune 14

system. Pregnancy can also chang e exposure patterns, for example, water 15

consumption in pregnant women is higher than the in the general population. 16

17

c) Immune Status

– T he immune system plays an important role in clearing 18

pathogens from the human body, which influences potential health effects. 19

Previous exposure may confer limited and/or short term protective immunity 20

(Frost et al., 2005) o r long lasting immunity (especially to viruses) . The converse 21

of this may also be true, that is, when individuals or sub populations that have not 22

previously been exposed to particular pathogens, infection and illness rates can be 23

higher than would otherwi se be anticipated. ―Traveler‘s diarrhea‖ is an observed 24

phenomenon that exemplifies this type of situation. In addition, individuals with 25

compromised immune systems who come into contact with pathogens may react 26

very differently from individuals with int act immune systems. Definitions of 27

sub populations or life stages included in the risk assessment should include the 28

criteria used to classify individuals as immunocompromised and may need to be 29

limited to specific identifiable types of immune defects. No te that children, 30

newborns, the elderly

, and pregnant women are also immunologically different 31

from healthy adults. 32

33

d) Natural Microbiota

– The competition that is provided by the presence of natural 34

microbiota influences the impact a pathogenic organism wil l exert on a host. 35

Prior treatment with antimicrobials that alter gut flora is a recognized risk factor 36

for infection with C. difficile and Salmonella

. 37

38

e) Nutrition

– The nutritional state of the host affects the immune system. 39

Malnourished individuals ten d to have weaker immune defenses than well 40

nourished individuals. 41

42

f) Clearance Mechanisms 18

– The human body has clearance systems to remove 43

foreign particles from tissues. For example, nasal and oral clearance systems can 44

remove airborne hazards and intesti nal clearance mechanisms can help remove 45

18 These may also be referred to as innate immunity.Page 58 Microbia l Risk Assessment Guideline

gastrointestinal pathogens. I ntestinal hypermotility may represent a host defense 1

mechanism against Giardia (Anderson et al., 2006). Gastric acidity is a barrier 2

that is a primary factor affecting the outcome of i nfections from food and 3

waterborne pathogens. Certain behaviors, for example smoking, may affect the 4

functionality of clearance mechanism s (e.g., mucociliary escalator); therefore, the 5

functionality of clearance mechanisms of various host sub populations s hould be 6

considered. 7

8

g) Genetic Factors

– The expression of certain genetic factors may increase an 9

individual‘s sensitivity to particular pathogens. Therefore, if genetic factors are 10

known for the organism being assessed, expression level differences coul d be 11

considered when characterizing the pathogen and performing dose

- response 12

modeling. Some genetic factors that influence pathogen dynamics may be linked 13

to race, which could be considered in the characterization of a hazard and dose

14

response modeling. 15

16

h) Preexisting Conditions

– Preexisting conditions may affect a host‘s response to a 17

pathogen, therefore, preexisting conditions should be considered, if possible. 18

Physical and emotional stressors may also influence host susceptibility. 19

20

i) Carrier Status (Pers istence in Population)

– The possibility of some humans to 21

serve as ―carriers‖ for pathogens needs to be considered when estimating the 22

potential spread of pathogens, especially when the a carrier may interact with 23

hosts who are considered susceptible. 24

25

j) Tr eatment Efficacy

– Whether or not effective treatment is available may be 26

important to risk assessment. Treatment efficacy can be a major determinant of 27

mortality. 28

29

k) Social and Behavioral Traits

– Social and behavioral traits primarily affect 30

exposure patt erns. For example, a relatively small proportion of the population is 31

responsible for consuming the majority of raw and partially cooked shellfish 32

(FDA, 2005; see age and behavior above). Social and behavioral traits may also 33

be associated with cultural and racial identities and may be important for specific 34

consideration in a risk assessment. 35

36

3. 10 How does Life Stage Affect Sensitivity to Infection and Disease 37

Manifestation? 38

39

Sensitivity to infection is based on both exposure to a pathogen and the integrity 40

of the immune system. Early life stages have a combination of factors that increase both 41

the possibility of infection and intensity and duration o f the disease. Young children 42

spend the first two years or more close to the ground whether crawling or playing. 43

Reliance on hands to mo ve around, hand

- to

- mouth activity, and eating with hands as 44

opposed to using utensils all raise the possibility for ex posure and ingestion of pathogenic 45

microorganisms compared with more mature individuals. 46Microbia l Risk Assessment Guideline Page 59

Draft

– Do not cite, circulate, or quote 1

A second issue with children is the immature development of their immune 2

systems. Infants have not had the exposure to the wide range of microbial stressors 3

needed t o afford protection. Newborn infants have passively acquired immunity from 4

their mothers, which dissipates over the ensuing months. This provides some protection 5

for newborn children, allowing them to develop the array of acquired immunity needed 6

for ful l protection. Nonetheless, young children tend to be more susceptible than older 7

individuals. 8

9

A significant consideration is the intensity and persistence of the disease in 10

newborn infants. In the absence of prior experience with a specific pathogen, th e body 11

requires more time to develop and process its immune response. Therefore, a pathogen 12

can elicit more pronounced and longer lasting effects from infection resulting in more 13

adverse outcomes in children compared with adults. I n developing countries, where 14

treatments are limited, children succumb to gastrointestinal infections at a higher rate 15

than in countries where timely medical intervention is widely available. 16

17

EPA‘s Risk Assessment Forum published Guidance on Selecting Age Groups for 18

Monitoring and Assessing Childhood Exposures to Environmental Contamina n ts (EPA, 19

2005). Based on physiological and behavioral milestones EPA recommends that children 20

be grouped by the following ages: 21

22

a) Less than 12 months old: birth to <1 month, 1 to <3 months, 3 to <6 months, and 23

6 to <12 months. 24

25

b) Greater than 12 months old: 1 to <2 years, 2 to <3 years, 3 to <6 years, 6 to <11 26

years, 11 to <16 years, and 16 to <21 years. 27

28

In addition to children, the elderly may also be more 29

susceptible to infections than healthy a dults. Risk assessments designed to include the 30

whole population should include discussion of the elderly, even if data specific to this 31

subgroup are not available. 32

33

3. 11 What Environmental Factors Can I Take into Consideration? 34

35

From the point of origin until it reaches the host, a given microorganism interacts 36

with the environment at various levels that influence its survivability and virulence. 37

Hence, at the point when the microorganism finally encounters the host, the ability to 38

cause adverse outcomes depends on the complex interactions (microorganism

- matrix, 39

matrix

- matrix, microorganism

- carriers/vectors) that the microorganism experienced 40

during transport. For exampl e, the survivability, virulence, transmission and successful 41

development of the diseases/adverse outcome for a given microorganism may be 42

influenced by the run

- off from animal husbandry or from wild animal habitats into water 43

bodies that can reach the targ et host directly through exposure to water or indirectly 44

through zoonotic transmission routes or contaminated foods. Thus, you should consider 45

the potential role of the non

- host environmental conditions in influencing changes in the 46Page 60 Microbia l Risk Assessment Guideline

properties of a microo rganism to render it potentially hazardous. These may include 1

matrices (air, water, soil, food), fomites, vectors, and carriers. As the physical, chemical, 2

and biological traits of the matrix can influence the microorganism‘s virulence and 3

survivability, a thorough characterization of the matrix becomes critical in a given risk 4

assessment

. Environmental factors can be considered with the epidemiological triad in 5

mind (Figure 1.2), which also provides context for segueing into exposure assessment 6

(see Cha pter 5). 7

8

9

10Microbia l Risk Assessment Guideline Page 61

Draft

– Do not cite, circulate, or quote 

## 4. DOSE

- RESPONSE ASSESSMENT 1

2

The goal of the ―dose

- response assessment‖ in MRA is to establish the 3

relationship between the dose of a pathogen that individuals or sub populations are 4

exposed to and the proba bility of adverse health effects (e.g., infection, illness, death) to 5

individuals or sub populations. Q ualitative evaluation (hazard characterization) of a 6

pathogen is also included in the conclusions drawn with regard to potential health 7

impa cts , particularly if data for a quantitative MRA are not available . From the estimated 8

quantita tive relationship (dose

- response model), the probabilit y of potential adverse 9

health effects of a given severity can be estimated from a given exposure to a pat hogen. 10

The exposure assessment and dose

- response assessment are combined in the risk 11

characterization step where risk due to a particular exposure for a defined sub population 12

and a defined hazard is described. It is important to note that because the dos e

- response 13

assessment is used in the context of an exposure assessment, it is critical that the dose 14

units used for quantitative estimates are comparable. 19 15

16

It should be noted up front that t here is no general accepted guidance regarding 17

when the available microbial dose response data are sufficiently representative for a 18

particular scenario, as the factors associated with each scenario are likely to be unique to 19

each case. 20

21

The information in this chapter is organized into two sections: 1) general 22

consider atio ns for dose

- response modeling (S ection 4.1); and 2) current practice in d ose

23

response modeling methods (S ection 4.2). The section on ―General Considerations for 24

Dose

- Response Modeling‖ discusses the technical application of using statistical models 25

to estimate health effects due to exposure. The second section focuses on the current ly 26

available quantitative dose

- response model s and on the development of new alternative 27

models

. 28

29

4.1 What is Dose

- Response Modeling and What are Some General 30

Consideration s for Dose

- Response Modeling? 31

32

Dose

- response modeling is the process of using mathematical relationships to 33

describe the probabilit y of an adverse health effect ( e.g., infection, illness ) occurring in 34

an individual or the frequency of an adverse health effect in a sub population when that 35

individual or sub population is exposed to a specific dose of pathogenic microorganisms. 36

The dose level may be measured in terms of a discrete number of organisms (e.g., 37

oocysts), colony forming units ( cfu ), plaque forming units ( pfu ) , or by molecular methods 38

that may estimate gene copies or cell equivalents

. A cfu represent s one to several viable 39

bac teria cells while a pfu represents one to several infectious viral particles. 40

A lternatively, a dose level may be specified by a n average administered or ingested dose 41

or represented by some other measure (e.g., median infectious dose [ ID 50 ] units). The 42

m ost common practice of dose

- response modeling has been the fitting of limited data sets 43

19 For example, the number of organisms ingested in a serving for a human volunteer trial might be

converted to number of organisms in a daily exposure. The units might go from event based in the dose

response assessment to a series of daily exposures over a lifetime for the exposure assessment.Page 62 Microbia l Risk Assessment Guideline

that have been derived from experimental trials to statistical models that are often, but not 1

always, biologically based. More recently, researchers have also been usi ng outbreak 2

data for dynamic dose

- response modeling of disease incidence in sub populations

, and 3

there is also a movement towards the development of models that incorporate the 4

inherent complexities found in any biological system. 5

6

4.1.1 How do I Choose B etween Modeling a Discrete Dose Versus an 7

Average Dose? 8

9

Pathogen doses are inherently discrete. While chemical doses are expressed in 10

mass units (e.g., mg/ kg), pathogen doses are usually expressed as counts of organisms 11

( e.g.

, oocysts/liter) or an average dose (e.g., mean cfu per serving )

. Although a group 12

exposure may be described in terms of average pathogen concentration , pathogens are 13

distributed in a p articular medium (water, air, food), such that each individual may not 14

receive exactly the same number of organisms. If the concentration of pathogens is very 15

low, some individuals could be exposed to zero pathogens, while others could be exposed 16

to one o r more pathogens. In this type of situation, the heterogeneity of the pathogen 17

distribution in the matrix can be very important. 20 18

19

While the average dose of pathogens is continuous and can take any value, the 20

actual number of organisms that an individual may consume is a discrete quantity. In the 21

context of a clinical feeding trial, t he distribution of pathogens or cfu in the delivery 22

matrix generally is assumed to be random but homogenous (i.e., with the same mean)

, 23

with the probability of exposure to a discre te quantity of organisms or cfu (0, 1, 2, etc

. ) 24

given by the Poisson distribution. Because distributional data is preferred in MRA over 25

point estimates (OMB, 2007b) , i f you only have data on the average dose, then you can 26

incorporate some assumption about the distribution (e.g., Poisson, extra

- Poisson, 27

exponential, mixture). Using Cryptosporidium parvum as an example, for an average 28

dose of 0.5 oocysts, most (60 percent ) individuals wou ld consume no oocysts, while 30 29

percent would consume a single o ocyst and 10 percent would consume 2 or more 30

oocysts. Drinking water exposures are usually low, often below an average dose of 1 × 31

10

- 4 organisms per liter, which essentially means that 1 out of 10,000 individuals would 32

ingest a single organism after drin king one liter of water at that concentration. 21 In 33

modeling feeding trial data where each dose is not directly counted, it is reasonable to 34

assume a Poisson distribution, but this may not be a reasonable assumption in modeling 35

outbreak data. 36

37

For low expo sures, the discrete nature of the exposure constrains the maximum 38

risk of infection to the probability of exposure. That is, in the previous ex ample, if the 39

pathogen were 100 percent infective, only one individual would become infected. You 40

20 Pathogens may clump together, particularly if the matrix has other components that aid clumping.

21 In all practicality, doses less than 0.01 represent dilutions of single organisms, with an insignificant

probability (though still not zero) of exposure to more than one organism. As a result the response is a

virtually

- linear functio n of dose at very low doses. Any function used for pathogen dose

- response must

follow the same ―rule.‖ Otherwise, a probability of infection greater than the probability of exposure could

be predicted.Microbia l Risk Assessment Guideline Page 63

Draft

– Do not cite, circulate, or quote should consid er using discrete dose

- response models in these cases, as the risk is usually 1

expressed as a unit

- pathogen infectivity, which is greatly influenced by the high Poisson 2

variability in the dose. In contrast, while exposure to pathogens in food is typically very 3

low, due to the potential for pathogen growth in some foods prior to consumption, the 4

exposure distribution may be extremely skewed with some frequency of exposures in the 5

range of millions to billions of organisms per exposure. In these cases, the P oisson 6

variability around the mean dose is trivial, such that doses in this range can be considered 7

to be continuous and strict adherence to discrete models is not required. In these cases

, 8

you can use quantitative dose

- response models based on the averag e pathogen dose to 9

estimate the probability of infection or illness (Haas et al. , 1999). 10

11

The Beta

- Binomial dose

- response model assumes that the exact number of 12

organisms ingested is known, which is suitable for a feeding trial in which each 13

administered dose has been enumerated. The exponential and beta

- Poisson models 14

assume that the number of organisms between subjects in a dose group is Poisson 15

distributed with a fixed mean (FAO/WHO, 2009). 16

17

4.1.2 What is the One

- Hit Model and Why is it the Preferred Model ? 18

19

O ne

- hit (or no

- threshold) dose

- response model s are generally the most relevant 20

for microbial dose

- response assessment . H owever, these models may not apply to al l 21

pathogens that cause illness by producing pre

- formed toxins in food and they may be 22

inappropriate for modeling illness and mortality

. 23

24

FAO/WHO (2003) observed that despite the traditional concept of a ―minimal 25

infectious dose‖ in the microbial literature , attempts to define the numerical value of a 26

threshold level of pathogens that must be ingested in order for the microorganism to 27

produce infection or disease have typically been unsuccessful. ―An alternative 28

hypothesis is that, due to the potential for microorganisms to multiply within the host, 29

infection may result from the survival of a single, viable, infectious pathogenic organism 30

(―single

- hit concept‖). This implies that, no matter how low the dose, there is always, at 31

least in a mathematical sense, and possibly very small, a non

- zero probability of infection 32

and illness‖ ( FAO/WHO, 2003 ). Similarly, NRC (2003) noted that unlike chemical risk 33

assessment, ―microbial dose

- response assessme nt for infectious pathogens do es not 34

produce any concept analogou s to the no observed adverse effect level (NOAEL) , since a 35

single microbial cell may (under the right circumstances) produce illness.‖ Finally, NRC 36

(2005) observed: ―In assessing risks attributable to exposure to microorganisms, it has 37

frequently been ass erted that there exists a threshold (minimum infectious dose) below 38

which there is no risk to the population. Such a concept is not consistent with the current 39

understanding of microbiological risk assessment...

. The no

- risk concept originated from 40

the fac t that in trials (either animal or human) , low doses of microorganisms often 41

produced no adverse effects in exposed subjects.... [However,] all animal and human 42

exposure data that have been subjected to dose

- response analysis are consistent with 43

models in which the dose intercept is zero and the value of the conditional dose

- response 44

relationship for one organism is non

- zero.‖ Empirically, it is impossible to distinguish 45

between a very low non

- zero risk and a true threshold. Risk assessors must rely on 46Page 64 Microbia l Risk Assessment Guideline

co ncept and theory to establish the most relevant dose

- response modeling approaches. 1

Therefore, in the ensuing discussion, the one

- hit dose

- response models are given 2

preference over alternative models typically used for chemical dose

- response modeling 3

(e.g. , log

- normal, log

- logistic, Weibull). 4

5

T he one

- hit model assumes that one infectious organism has the potential to cause 6

infection. When an individual organism is ingested, the probability that the organism 7

defeat s the host barriers and initiat es an inf ection may be represented by a unit

- infectivity 8

measure. This measure is termed ― r ‖ by convention, as r is the rate constant in the most

9

commonly used exponential dose

- response model. The exponential distribution is based 10

on the assumption that each orga nism is capable of initiating an infection and behaves 11

independently from other organisms within the host, leading to a binomial probability of 12

infection. The assumptions of a Poisson pathogen distribution and a binomial probability 13

of infection lead to a family of models referred to as one

- hit models, where the name 14

relates to the concept that only a single organism is necessary to cause infection. 15

Different pathogen distributions can be assumed in addition to the Poisson. If there is 16

reason to believe that the pathogens are not uniformly distributed in the exposure medium 17

(e.g., clumped), a negative binomial or other skewed distribution can be used (Haas et al., 18

1995; Teunis et al., 2008b), although the numerical algorithms become more 19

complicated. Fro m a modeling perspective

, the one

- hit model means that there is a non

20

zero probability that any given organism will survive the host defenses to initiate an 21

infection, no matter how low the probability may be. Unit

- infectivity probabilities can 22

range from 1 × 10

- 10 to greater than 0.1 (Teunis et al., 1996; Haas et al., 1999). Although, 23

conceptually based on biologically plausible mechanisms, most of these models do not 24

rely on independently validated parameters describing the mechanisms model the 25

underlyi ng physiological processes explicitly, but rely on fitting curves to empirically 26

observed data. The potential for applying physiologically

- based models is discussed in 27

section 4.2.8

. 28

29

The one

- hit concept also applies to the production of illness in that, once an 30

individual is infected, progression to illness can proceed without additional exposure. 31

Replication within the host of that single pathogen from the initial exposure would 32

eventually result in enough pathogens to produce illness symptoms. However

, the 33

standard one

- hit dose

- response models are typically used for assessing the risk of 34

infection rather than illness (or mortality)

. M orbidity and mortality may be expressed in 35

only a small fraction of infected individuals. Since morbidity is currently most 36

commonly assumed to be independent of dose (but conditional on infection ) it is best to 37

use a morbidity or mortality ratio applied to the risk of infection (Haas et al., 1995). As 38

a special case ( ―quorum sensing‖), illness may not occur until the nu mber of organisms 39

reaches a critical mass at which time they release the toxins resulting in illness. 40

However, the increase in the number of organisms is a result of growth within the host 41

rather than an increase in the exposure dose. The concept of dens ity dependent quorum 42

sensing is distinct from a threshold for administered dose, because of the possibility, 43

however small, that a single ingested organism may survive the multiple barriers in the 44

gut to become established and reproduce ( FAO/WHO, 2003). T hreshold models have not 45

been demonstrated to provide significant improvements in fit over the exponential and 46Microbia l Risk Assessment Guideline Page 65

Draft

– Do not cite, circulate, or quote beta

- Poisson models, but their use has been advocated on the basis of analysis of the 1

infection process and interpretation of epidemiological dat a. 2

3

4.1.3 What Important Factors Can I Consider in Dose

- Response 4

Assessment? 5

6

Exposure R oute 7

8

The route of exposure can have a significant bearing on the dose

- resp onse curve

. 9

As noted above, for infection to occur and result in a disease state

, the organism must 10

penetrate the host ‘ s defenses whose capability varies with tissue type

. You need to be 11

aware that route of exposures can influence both the slope of the d ose

- response curve as 12

well as the manifestation. For example, Adenovirus can be highly infectious through an 13

inhalation route of exposure, and appears to be less infectious through ingestion. 14

Therefore, matching the route of exposure, with appropriate dos e

- response information is 15

important. Discussion of the implications of using the available dose

- response dataset for 16

broader exposure scenarios is helpful. 17

18

In chemical risk assessment, route

- to

- route extrapolation of internal dose for 19

systemic effects ca n be performed if adequate toxicokinetic data exist. 22 For pathogens, 20

although systemic involvement can be an important factor, some pathogens can only 21

infect specific tissue types

. O ther pathogens can infect many different tissue types and 22

have a differe nt dose

- response and health endpoint for those different tissues. For 23

example, Cryptosporidium usually infects the gastrointestinal tissues, but in 24

immun o compromised patients

, it can infect other tissues including lungs and result in a 25

different set of sy mptoms (O‘Donoghue, 1995). Usually the most obvious concern is 26

where the route of exposure leads directly to the most susceptible tissues, such as 27

diarrhea from ingestion, pneumonia from inhalation, or dermal lesions from skin contact. 28

However for pathog ens that cause illness through release of toxins, the host tissue that is 29

most susceptible to the toxin may be remote from the physical location of pathogen 30

replication. 31

32

Exposure Medium 33

34

The nature of the exposure medium can influence the probability that an 35

individual pathogen will survive host defenses and initiate an infection. For oral 36

exposure, pathogens can be ingested in food, water, or from direct contact with fomites or 37

infected individuals. The primary factor in this process is the initial line of defense 38

against the pathoge n

— stomach acid and digestive enzymes. If the ingestion medium 39

serves to raise the pH in the stomach content, the probability of pathogen survival is 40

enhanced for many organisms, perhaps by orders of magnitude

, depending on t he extent 41

of acid buffering. The nature of the food matrix, for example, with respect to food 42

structure and fat content

, can vary considerably in enhancing or limiting survival of 43

pathogens in the food matrix and in the hos t gastrointestinal ecosystem (Ro ss, 2008). In 44

22 Chemical risk assessors have defined many differe nt aspects of dose, such as potential dose, applied

dose, absorbed dose, internal dose, and delivered or biologically effective dose.Page 66 Microbia l Risk Assessment Guideline

addition, taking stomach

- acid reducers for acid

- reflux disease can provide protection for 1

the pathogen. Ingestion of drinking water, on the other hand, offers no protection from 2

stomach acid. 3

4

In the dose

- response documentation, you shoul d discuss possible effects of 5

different matrices relevant to the assessment

. You could compare the delivery matrix 6

used in generating dose

- response data from a human feeding study to the matrix being 7

considered in the exposure scenario. For example

, if t he dose

- response data is from 8

feeding trials using a water matrix and the exposure scenario in the risk assessment is 9

juice, then you should clearly describe the difference and elaborate on what the potential 10

implications of those differences might mean fo r the risk assessment. 11

12

Pathogen 13

14

During hazard identification the pathogen of concern is defined . In the dose

15

response assessment, discussion of whether or not the pathogen of concern matches the 16

pathogen for which dose

- response data is critical . This i ssue is important because 17

substantial variability in virulence and infectivity has been shown for closely related 18

pathogens. For example, the relative infectivity of the Salmonella enterica serotypes 19

(Coleman and Marks, 2000; Soller et al., 2007) and C. p arvum isolates ( Haas et al, 1999; 20

Messner et al, 2001; Teunis et al., 2002; Englehardt and Swartout, 2004 ) used during 21

human dose

- r esponse challenge studies varied by several orders of magnitude

. Although 22

you may only have dose

- response data for one or a few isolates of a species, risk 23

managers may be interested in the species as a whole (e.g.

, the C. parvum example 24

above) or the genus (given that a number of Cryptosporidium spp. may infect 25

humans)(Xiao, 2010) . Isolates only represent a small fraction of the genetic diversity of 26

the species that is likely to occur in nature. Discussion of the implications of using the 27

available dose

- response dataset for broader exposure scenarios is helpful . There is no 28

general guidance regarding when the available dose r esponse data are sufficiently 29

representative for a particular scenario, as the factors associated with each scenario are 30

likely to be unique to each case. 31

32

Data on the actual pathogen of concern are preferred over data on surrogate 33

organisms

. 23 In cases wh ere data on the pathogen of concern are not available, d ata on 34

surrogate organisms can be used if solid biological evidence, such as common virulence 35

factors , can support that choice

. T he biological basis for the use of the surrogate must be 36

clear (FAO/WH O, 2003)

. 37

38

Haas et al

. (1999 ) provides a method for testing whether differences among 39

strains are statistically significant. It i s also important to determine whether the strains 40

can be considered a representative sample from the pathogen of concern or an extreme 41

case. In cases where the strain is representative , you may want to use a mixed model with 42

random strain effects. In cases where the strain is an extreme example , a bounding 43

approach can be used (Vose, 2008). 44

45

23 ―Surrogate organism‖ synonyms include ―index pathogen‖ and ―reference pathogen.‖Microbia l Risk Assessment Guideline Page 67

Draft

– Do not cite, circulate, or quote Host 1

2

During hazard identification yo u should discuss host factors that are relevant for 3

the risk assessment scenario. Given the current state of knowledge, quantitative 4

microbial dose

- response assessment for humans requires some human pathogenicity data. 5

When using dose

- response data from human tr ia ls, you should discuss the characteristics 6

of the sub population in the trials compared to the sub population defined in the scope of 7

the risk assessment. You can specifically discuss the types of individuals that are 8

explicitly represented in the human trials (e.g., healthy adults) and individuals that were 9

excluded from the trials (e.g.

, children, elderly, immune compromised, and pregnant 10

women). Describe the potential implications to the different sub populations or life 11

stages , and point out an y tools that you used to compensate for the differences, such as 12

information from epidemiological data from outbreak situations. Also discuss the 13

likelihood that individuals in the trials may have had some immunity to the pathogen 14

being evaluated. 15

16

Altho ugh human data are preferable, a nimal studies and in vitro studies may 17

provide useful information on host

- pathogen interactions (FAO/WHO, 2003)

. The 18

strengths and limitations of those data, within the context of your risk assessment 19

scenario, should be cl early explained. Some pathogens have evolved a narrow host 20

range, while others have evolved a broad host range. Many important pathogens are 21

host

- specific, but over 60 percent of human pathogens have multiple domestic mammal 22

hosts (Cleaveland et al., 200 1). Am ong pathogens of wild primates, only 10 percent of 23

bacteria, 13 percent of viruses, and 28 percent of protozoa are host

- specific, and more 24

than 100 pathogens (including 19 bacteria and 30 viruses) infect both wild primates and 25

hu mans (Pedersen et al ., 2005). Viruses mutate rapidly and can ―jump‖ to new host 26

species more easily than bacteria or protozoa. On the other hand, protozoa can be much 27

less discriminating in their choice of host species, often infecting humans after passing 28

through wild and domesticated animal species. However, the disease manifestations can 29

be quite different, implying that different pathogenic mechanisms are in operation. As an 30

example, C. parvum typi cally causes diarrhea in humans with no systemic involvement 31

but kills ( immunocompromised) mice from generalized systemic distribution with no 32

diarrheal symptoms. One host cannot be used as a surrogate for another in all respects, 33

and extrapolation from surrogate host data may require adjustments. Therefore, when 34

surrogate a nimal models are used, the biological basis for and limitations of the use of the 35

surrogate must be clear (FAO/WHO 2003)

. 36

37

Endpoint 38

39

The endpoints that are typically modeled in MRA are infection, illness

, and death. 40

Infection is the most immediate health effect for direct modeling

, as it is the first 41

manifestation of exposure to a pathogen. However, infection is difficult to assess in 42

humans if there are no clinical symptoms. Infection is generally equated with 43

colonization of some tissue, either externa lly or internally . Animal studies can include 44

specific tissue analysis of sacrificed groups at intervals following the original inoculation 45

to analyze the clearance time and/or the level of colonization ( EPA , 2007 a ). However, 46Page 68 Microbia l Risk Assessment Guideline

colonization per se is diffi cult to measure in humans, so typical markers (measurement 1

endpoints) of infection such as sheddi ng of the pathogen in feces or urine, presence of 2

antibodies in the blood (seroconversion) or clinical symptoms are often used to determine 3

if infection has oc curred. Although (asymptomatic) infection has no direct adverse health 4

impact, it can be crucial in determining the risk of illness and plays an important role 5

when estimating the impact of secondary transmission. Illness and mortality are the 6

primary en dpoints of health concern. Illness endpoints could range from gastrointestinal 7

distress to long

- term sequelae. The risk s of illness and death generally are estimated 8

from the risk of infection by applying morbidity or mortality ratios, but can be modeled 9

directly in certain circumstances. For example, the most commonly employed dose 10

response relationship for the ingestion of Salmonella through a waterborne route of 11

exposure is for an endpoint of illness, not infection. Define the health endpoint of 12

conc ern and specifically relate that definition to the clinical case definition used in any 13

utilized dose

- response studies. If there are any differences between the endpoint 14

definition for the risk assessment and the case definition for data from trials or ou tbreak 15

studies, you should describe the implications of those differences to the interpretation of 16

the risk assessment results. 17

18

Inconsistencies in illness definitions can introduce uncertainties. For example, the 19

definitions of diarrhea may differ slight ly between different clinical trials or 20

epidemiological studies and may be based on moisture context of stools . Illness 21

definition endpoints can be quite complex. For example, EPA‘s bacterial ambient water 22

quality criteria for recreational waters are bas ed on highly credible gastrointestinal illness 23

as defined as ―any one of the following unmistakable or combinations of symptoms 24

( within 8 to 10 d ays of swimming) : (1) vomiting ; (2) diarrhea with fever or a disabling 25

condition (remained home, remained in be d or sought medical advice because of 26

symptoms) ; (3) stomachache or nausea accompanied by a fever‖ (Dufour, 1984). Note 27

that individuals with only diarrhea are not ―counted‖ as a case with the above definition. 28

Other studies may include diarrhea alone, w ithout fever as a valid case of gastrointestinal 29

illness. F or example, norovirus does not typically cause fever. You should pay close 30

attention to the nuances of how endpoints are defined and discuss the impact any 31

differences may have on the risk assess ment. 32

33

How treatment can impact the health outcome should be discussed. Some 34

infections are treatable and others are not. Treatment efficacy could influence 35

determining the most relevant health outcome. 36

37

Sources of Data 38

39

C learly document what sources of data were considered , utilized , and omitted, 40

and provide justification for those decisions. The most precise human data are the direct 41

pathogen

- challenge studies (clinical feeding trials), in which human volunteer subjects 42

are fed known doses of a specifi c ―enteric‖ pathogen and observed for gastro

- intestinal 43

symptoms over a period of time. These data can be modeled directly to obtain dose

44

response relationships and parameters for application to specific human exposure 45

scenarios. For ethical reasons, dir ect pathogen

- challenge studies are typically limited to 46Microbia l Risk Assessment Guideline Page 69

Draft

– Do not cite, circulate, or quote healthy adult subjects challenged with a pathogen that has well

- characterized health 1

outcomes that are no more serious than temporary diarrhea that is self

- clearing. H uman 2

pathogen

- challenge data has been used in EPA regulations to predict the risk of g iardiasis 3

(EPA, 1989, 1998b, 1999) and c ryptosporidiosis (EPA, 2006a). Although data from 4

clinical feeding trials are carefully collected and doc u mented, there may be limitations to 5

extrapolating from experimental conditions that need to be addressed (e.g., subjects 6

limited to healthy adults, use of non

- wild

- type pathogen strains, dose delivery matrix 7

buffered to increase the likelihood of pathogen survival)

. 8

9

Human dose

- response information can also be obtained from epidemiological 10

data (primarily retrospective outbreak analyses ). Although such data may be more 11

representative of the actual host

- pathogen

- matrix combination of concern, there is usually 12

considerable uncertainty in the dose estimate, numbe r of exposed or number of 13

responders, or combinations of those variables. One significant advantage that 14

epidemiological data have over clinical feeding trials is the potential for evaluating the 15

relative risk of sensitive populations, as was done for Esc herichia coli O157:H7 (Teunis 16

et al.

, 2004, 2008 a ). 17

18

Animal dose

- response data have been used to estimate the human dose

- response 19

curve and to estimate the innate pathogen variability across strains. However, these data 20

are difficult to translate direct ly to human dose

- response and require critical evaluation 21

prior to use due to all of the uncertainties associated with interspecies extrapolation. 22

Epidemiological information also can be used to calibrate ( ―anchor‖ ) dose

- response 23

curves derived from anima l data with respect to the relevant human response range; this 24

was done in the FDA/ USDA/CDC Listeria monocytogenes risk assessment by shifting 25

the mouse mortality dose

- re s ponse curve based on human mortality rates attributed to L

. 26

monocytogenes (FDA/ USDA/C DC , 2003). In addition, the variability of median lethal 27

doses ( LD 50 s ) in mice across Listeria strains was used as an estimate of the pathogen 28

virulence variability. The assumption was made that the pathogen virulence variability 29

was similar to the level of variability likely to be encounte red in human exposures and 30

was useful for dose

- response uncertainty analysis. Note that this use of animal data is not 31

in lieu of human data, but is used to support assumptions about characteristics of human 32

data, such as potential range of variability. For more information on the use of animal 33

data in MRA see EPA (2009a). 34

35

Outbreak investigations can often provide valuable information about the 36

etiologic agent. A pathogen‘s ability to produce an outbreak depends on sp ecific 37

characteristics such as ability to survive in the environment and rate of growth or die off, 38

potential to cause disease at a given dose, most likely transmission route, and capacity to 39

spread through person

- to

- person contact. Therefore, studying th e details of outbreaks can 40

help risk assessors develop exposure scenarios for specific pathogens

, and data from 41

outbreaks can be an important comparison for dose

- response models based on human 42

feeding trials or animal models. The major limitation to outbr eak investigation data is 43

that investigators often collect a narrow range of information

. T heir main objective is to 44

rapidly identify the vehicle and prevent additional infections. Occasionally, data from 45

concurrent drinking water testing are available, or frozen or unopened suspect food 46Page 70 Microbia l Risk Assessment Guideline

remains for sample collection and accurate identification and enumeration of pathogens. 1

If actual levels of food or water contamination can be measured or estimated, it may be 2

possible to estimate the dose

- response relati onship from outbreak data. A n outbreak that 3

is characterized by a low attack rate in a very large population may provide an 4

opportunity to define the host

- response to very low doses of a pathogen (Teunis et al., 5

2004). 6

7

Like outbreak data, annual surveilla nce statistics provide a way to evaluate MRA 8

models. The initial results of a MRA model can sometimes be cross

- checked by 9

comparison with public health surveillance data. The accuracy of dose

- response models 10

may be assessed by combining them with exposur e estimates known to be realistic and 11

determining if the results approximate the incidence of illness estimated from 12

surveillance data, taking into account the uncertainty due to under

- reporting . Using 13

annual disease statistics in modeling dose

- response a nd exposure estimates implicitly 14

includes the entire population and the wide variety of factors that can influence the 15

response. Another benefit is that surveillance databases may have sufficient detail to 16

analyze special subpopulations or life stages suc h as the elderly or the 17

immunocompromised. If information is available, the surveillance summaries or a series 18

of reported foodborne or waterborne outbreaks can also identify the etiologic agents 19

causing disease outbreaks and often the sources of the cont amination. Consultation with 20

public health surveillance experts can provide a ―reality check‖ on the preliminary results 21

of risk ranking and other quantitative MRAs. 22

23

4.1.4 How Can I Model the Spread of Disease in the Population? 24

25

In addition to primary transmission via contaminated media (e.g., water or food), 26

many microbial pathogens can also be transmitted via person

- to

- person contact and cause 27

infection and disease. 24 This route of microbial exposure, occurring from an infected 28

person rather than from contaminated media is also referred to as ―secondary 29

transmission.‖ You can discuss any known features of secondary transmission when 30

characterizing the microbial hazard, regardless of whether the scope of the risk 31

assessment includes modeling secondary transmission. The ability with which an 32

organism can be transmitted from an infected individual to an individual that is 33

susceptible to infection is an important considera tion and should be evaluated during the 34

exposure assessment if possible

. 35

36

Secondary cases (often represented in epidemiological stud i es by a secondary 37

attack rate) generally refer to cases that occur among contacts, within the incubation 38

period of the pat hogen, and following exposure to a primary case. In some cases, direct 39

person

- to

- person transmission cannot be distinguished from contamination of the 40

immediate environment (e.g., toddlers sharing toys versus direct physical contact during 41

play). Dependi ng on the purpose of the assessment , it may be appropriate that the 42

24 Some situations blur the line betw een secondary transmission and environmental transmission. For

example, infections due to a primary case causing an outbreak of cases in a daycare setting is usually

classified as secondary transmission even though transmission may be from both direct huma n contacts and

contaminated objects, food, or water exposures.Microbia l Risk Assessment Guideline Page 71

Draft

– Do not cite, circulate, or quote definition of secondary transmission include infections that result from propagation of the 1

specific exposure of interest, but not encompass distant transmissions (separated by time 2

and/or space) that may be more appropriately considered to result as a function of person

3

to

- environment

- to

- person transmission. Temporal and spatial limitations can be 4

specifically noted in the definition of secondary transmission. You can discuss the full 5

ra nge of scenarios that qualify as both primary and secondary transmission. The above 6

definition of secondary transmission is limited to avoid overlap with pathogen occurrence 7

in the environment (person

- environment

- person), although people are, of course, p art of 8

the environment. However, the potential for reintroduction of the pathogen into the 9

exposure media could also be within the definition of secondary transmission. 10

11

The degree to which the exposed population is susceptible to infection and illness 12

is an important factor in deciding whether to explicitly model secondary transmission in a 13

risk assessment. Population susceptibility and immunity change dynamically as a 14

population is exposed to a pathogen. Moreover, since many infections are asymptomatic

, 15

it is common for pathogens to be transmitted from person to person during asymptomatic 16

infections or during asymptomatic periods of an infection (before or after symptoms), 17

when infected individuals interact normally with susceptible individuals. Previo us 18

infection by the same organism often confers temporary or permanent immunity

, 19

although individual immunity may be partial (e.g., resistance to infection may be 20

overcome when challenged by the same pathogen at a high dose or by a genetically 21

different st rain) . If a significant proportion of the population is immune, such as from an 22

immunization program, secondary spread of disease can be virtually prevented. 23

24

MRA models can be configured to account for secondary transmission and 25

immunity in a populatio n through the use of a dynamic model (Anderson and May

, 26

1991) . These models, which can take several forms (deterministic or stochastic)

, 27

characterize the dynamic epidemiological status of the population (e.g. susceptible to 28

infection, symptomatic infectio n, immune). Static MRA models do not, by their nature 29

consider secondary transmission , although dose

- response parameters derived from static 30

models may be incorporated into dynamic models. You can indicate if and how 31

secondary transmission is included in the assessment. Inclusion of secondary 32

transmission in MRAs often provides non

- intuitive results; therefore, if secondary 33

transmission and other innate characteristics of infectious disease transmission are not 34

included in the assessment, provide a sound justification for this decision. 35

36

4.1.5 What Can I Address for Each Model to Improve Transparency? 37

38

To promote transparency in presenting the dose

- response assessment, you can 39

address the following points for each model presented: 40

41

a) Assumptions 42

1) Clearly state models‘ key assumptions 43

2) Discuss assumptions inherent when extrapolating to doses lower than 44

those used in studies. 45Page 72 Microbia l Risk Assessment Guideline

3) Discuss flexibility in approaches to the dose

- res ponse relationship 1

depending upon the pathogen being considered and the assumption about a 2

no

- threshold effect (i.e., can it be assumed that one organism is sufficient 3

to produce infection?). 4

b) Applicability of Models 5

1) Discuss the biological rationale for the model 6

2) Discuss the applicability of each model to various exposure situations 7

3) Discuss limitations of models 8

4) Articulate strengths/weaknesses and advantages/disadvantages of the 9

models 10

c) Results 11

1) Discuss the type of information that the various models are expec ted to 12

provide. 13

2) Discuss the use of likelihood methods to compare how well dose

- response 14

models fit the data 15

16

4.2 What is Current Practice in Quantitative Dose

- Response Modeling for 17

Microbial Illness ? 18

19

This section briefly summarizes some common dose

- response models and how 20

those models have been used in previous MRAs. It also discusses the output of dose

21

response modeling and evaluating uncertain ty and accounting for life stages and 22

subpopulations. 23

24

4.2.1 What Models C an I Use for Microbial Dose

- Response Assessment? 25

26

Dose

- response models are mathematical func tions that yield a probability of an 27

adverse health effect as a function of dose. Numerous dose

- response relationships for 28

microbial endpoints have been published in the peer

- reviewed literature. No 29

comprehensive summary of dose

- response models is availa ble for all human pathogens. 30

However, a thorough summary of dose response models for waterborne pathogens can be 31

found in Table 4.1. The two most commonly used dose

- response models are the 32

exponential and beta

- Poisson . Several alternative models have al so been proposed as 33

alternatives for MRA

, including two

- parameter models such as log

- normal, log

- logistic, 34

and extreme value models (Pinsky, 2000), and three

- parameter models, such as Weibull 35

gamma (Farber et al., 1996), exponential gamma, Weibull exponent ial, and the shifted 36

Weibull model (Kodell, 2002). However it should be noted that t here is no single 37

selection criteria for dose

- response models that is universally used. Several criteria that 38

could be used are provided in Section 4.2.3. You should exp lain the logic behind your 39

model selection and strengths and limitations of the model selection. 40

41Microbia l Risk Assessment Guideline Page 73

Draft

– Do not cite, circulate, or quote The models discussed in this section estimate risks for exposed individuals; thus, 1

they are known as individual risk models. 25 Population risks (the incidence of disease 2

among a group of exposed individuals) are generally constructed by combining the 3

results of individual risk models with estimates of the distribution of doses in the exposed 4

population ( EPA, 200 9 a ). 5

6

7

25 Note that ―individual‖ risk models may have as their outputs probability distributions of risk that can be

interpreted to reflect (1) uncertainty in infectivity of the agent tested, and/or (2 ) variability in individual

susceptibility among the experimental subjects.Page 74 Microbia l Risk Assessment Guideline

Table 4.1 Overview of Dose

- Response Rela tionships for Waterborne Pathogens a 1

(Source: EPA 2009 a ; Adapted from McBride et al., 2002) 2

Microorganism Model Parameters b Reference (s)

Adenovirus 4 Exponential r = 0.4172 c Crabtree et al., 1997

Haas et al., 1999

Campylobacter

jejuni h,i Beta

- Poisson α = 0.145 β = 7.59 Haas et al., 1999

Medema et al., 1996

Teunis et al., 1996

Infection:

Hypergeometric beta

Poisson

Illness: Conditional on

infection g α = 0.024 β = 0.011

- 9 r = 2.44x10 8 Teunis et al., 2005

Coxsackievirus Exponential r = 0.0145 Haas et al., 1999

Cryptosporidium Exponential r = 0.0042 Haas et al., 1996, 1999

r = 0.077 d Okhuysen et al., 1999

r = in the range 0.04 to 0.16 EPA, 2006a

Generalized beta

Poisson for Illness α = 0.060 β = 0.095 Englehardt and Swartou t,

2006

Echovirus 12 Exponential r = 0.0128 Haas et al., 1999

Beta

- Poisson α = 0.401 β = 227.2 Teunis et al., 1996

α = 0.374 β = 186.69 Regli et al., 1991

Rose and Sobsey, 1993

α = 1.3 β = 75 Rose and Gerba, 1991

Ent amoeba coli Beta

- Poisso n α = 0.1008 β = 0.3522 Haas et al., 1999

Escherichia coli

(pathogenic

strains) Beta

- Poisson α = 0.1778 β = 1.78x10 6 Haas et al., 1999

E. coli O157:H7 Beta

- Poisson e α = 0.248 β = 48.80 Teunis et al., 2008a

Hypergeometric beta

Poisson α = 0.084 β = 1.44

(children)

α = 0.050 β = 1.001

(adults) Teunis et al., 2004

Giardia lamblia Exponential r = 0.0199 Haas et al., 1999

Regli et al., 1991

Rose and Gerba, 1991

Rose et al., 1991

Teunis et al., 1996

Hepatitis A virus Exponential r = 0.5486 f Haas e t al., 1999

Legionella Exponential r = 0.06 Armstrong and Haas,

2008

Norovirus Infection (with

aggregation):

Hypergeometric function

1 F 1

Illness: Conditional on

Infection g α = 0.040 β = 0.055

= 2.55×10

- 3 r= 0.086 Teunis et al., 2008b

Poliovirus I B eta

- Poisson α = 0.1097 β = 1524 Regli et al., 1991

Rose and Sobsey, 1993

α = 15 β = 10 00 Rose and Gerba, 1991Microbia l Risk Assessment Guideline Page 75

Draft

– Do not cite, circulate, or quote Microorganism Model Parameters b Reference (s)

Exponential r = 0.009102 Haas et al., 1999

Regli et al., 1991

Rose and Sobsey, 1993

Poliovirus III Beta

- Poisson α = 0.409 β = 0.788 Ros e and Sobsey, 1993

α = 0.409 β = 0.788 Regli et al., 1991

α = 0.5 β = 1.14 Rose and Gerba, 1991

Rotavirus Beta

- Poisson α = 0.26 β = 0.42 Gerba et al., 1996

α = 0.2531 β = 0.4265 Haas et al., 1999

Regli et al., 1991

Rose and Sobsey, 1993

α = 0.232 β = 0.247 Rose and Gerba, 1991

Hypergeometric beta

Poisson α = 0.167 β = 0.191 Teunis and Havelaar,

2000

Salmonella Beta

- Poisson α = 0.33 β = 139.9 Rose and Gerba, 1991

Gompertz log ln(a) in the range 29 to 50

b = 2.148 Coleman and Marks, 2000

Coleman et al

., 2004

Soller et al., 2007

Generalized linear mixed

models and

fractional polynomials of

dose β 0 = 0.323 β 1 = 5.616

β 2 =

- 8.462 β 3 =

- 7.782

d 2 = 0.780 Bollaerts et al., 2008

Salmonella (non

typhoid) Beta

- Poisson α = 0.3126 β = 2884 Haas et al., 1999

Salmonella T yphi Fractional polynomials β 1 =

- 18.1425

β 2 = 22.5300×10

- 5 Namata et al., 2008

Beta

- Poisson α = 0.1086 β = 6,097 Haas et al., 1999

α = 0.21 β = 5,531 Rose and Gerba, 1991

Shigella Beta

- Poisson α = 0.21 β = 42.86 Haas et al., 1999

Vibrio cholera e Beta

- Poisson α = 0.25 β = 16.2 Haas et al., 1999

a These calculations are based on available data that have used particular pathogen strains processed in particular ways. 1

Where more than one strain of an organis m has been studied in clinical trials, a wide range of infectivities can be 2

discovered . Therefore it must be recogniz ed that these calculations can carry a substantial degree of uncertainty. 3

b For the exponential distribution N 50 = 0.693/r; for the b eta

- Poi sson distribution N 50 = * (2 1/

- 1). Values are unitless. 4

c Developed for inhalation exposure to adenovirus 4 aerosols. 5

d Estimated based on ID 50 reported for the Texas A&M University ( TAMU ) isolate. 6

e Represents a meta

- analysis of seven outbreaks and adjusting for heterogenei ty. Alpha/beta pairs derived via MCMC 7

analyses are available from Dr. Teunis. Use of those pairs is preferred to the use of the values shown in this table 8

f Corresponding dose units are grams of feces. 9

g Dose

- response relation for the conditional probabi lity of illness in infected subjects = 1

– (1+ CV)

- r , where and r 10

are shown in the Table ; CV is the dose (concentration × volume)

. 11

h An alternate dose

- response mod el is proposed by Brynestad et al. (2008). 12

i Many of these models have been critiqued in the literature. For example, Coleman et al. (2004) suggest th e dose

13

response models for Campylobacter identified in this table do not account for strain variability sufficiently and suggest 14

the need for development of more detailed mechanistic models. 15

16

An overview of Exponential, beta

- Poisson, deterministic, and Bay esian 17

hierarchical m odels is provided below and a summary of p eer reviewed dose

- response 18

models is presented in Table 4.1. 19

20

21

22Page 76 Microbia l Risk Assessment Guideline

The Exponential Model 1

2

The single

- hit family of dose

- response models was described previously (Section 3

4.2.1) as the most relevan t for microbial dose

- response assessment. The simplest of the 4

single

- hit family of models is the exponential model (Equation 1). 5

6

p = 1− e − r d ( Eq. 4. 1) 7

8

Where: 9

p is the cumulative probability of infection in the exposed population 10

d is the average pathogen dose in infectious units (organisms) 11

r is the probability of infection given ingestion of one organism 12

13

For the exponential model, r is a constant for the interaction of any given 14

pathogen and host species. That is, each infectious particle with in each host is assumed 15

to have the same probability of survival. There is no inter

- individual variability in the 16

host

- pathogen interaction: the exponential model assumes that the same probability of 17

infection applies to every individual in the population . Despite this unrealistic

- sounding 18

assumption, the exponential model provides a good fit for a number of the human 19

pathogen

- challenge data sets (Teunis et al., 1996; Haas et al., 1999). The primary 20

advantage of the exponential model is its computational simplicity. The primary 21

disadvantage is that is does not account for inter

- individual variability in the population. 22

23

The beta

- Poisson Model 24

25

The primary limitation of the exponential model (no variability in r ) is 26

partially 26 overcome using the beta

- Pois son model

, by assigning a distribution to r 27

to represent the variability in the pathogen

- host interaction. The most common 28

distribution applied to r is the beta, giving rise to the beta

- Poisson model (more 29

strictly, the beta

- exponential). As r is a proba bility itself, the assigned distribution 30

must have a range of 0 to 1. The beta distribution is the most flexible of such 31

distributions, including shapes similar to Gaussian (normal), triangular, exponential, 32

power law, uniform

, and bimodal. The unit infe ctivity r

- value is the mean of the 33

distribution, which is readily calculated from the parameters. The two

- parameter beta

34

Poisson does not have a fixed slope and is more flexible than the exponential. The beta

35

Poisson still follows the rules (does not exc eed the probability of exposure at low dose)

, 36

but is more biologically plausible than the exponential and will fit better to data with 37

higher variances. The beta

- Poisson is a function of the confluent hypergeometric 38

function, which is a sum of an infinite number of terms and has no simple closed

39

form mathematical representation (Abromowitz and Stegun, 1964). T he beta

40

Poisson cumulative probability is given by Equation 2. 41

42

1

– M(

, , - d) ( Eq. 4. 2) 43

44

26 The distribution on r does not distinguish between pathogen and host variability.Microbia l Risk Assessment Guideline Page 77

Draft

– Do not cite, circulate, or quote where M is the confluent hypergeometric function (the 1 F 1 form), and are the beta 1

distribution parameters, and d is the mean dose in pathogen infectious units. The solution 2

is estimated numerically. 3

4

The Pareto II distribution, commonly called the beta

- Poisson in the literature, was 5

shown by Furumot o and Mickey (1967) to approximate the exact theoretical beta

- Poisson 6

model, and has found wide usage. The Pareto II distribution function for dose response is 7

given by Equation 3. 8

9

1

– (1 + d

/ ) ( Eq. 4. 3) 10

11

where d is the dose, and and are parameters corresponding to the beta distribution 12

parameters for specific ranges, t he approximate form being valid for parameter values 13

>> 1 and << ( Teunis and Havelaar, 2000 ). Outside of this range, the Pareto II can 14

substantially overestimate the risk, sometimes predicting a probability of infection greater 15

than the probability of exposure at low doses (Teunis and Havelaar, 2000). The term 16

―beta

- Poisson‖ will be used, henceforth, with reference to the exact form, while the 17

analytic approximation w ill be referred to as the Pareto II. Both forms have been used 18

for the gastro

- enteric infection endpoint (Haas et al., 1996; T eunis and Havelaar, 1999; 19

Englehardt and Swartout, 2004). Thus, the beta

- Poisson is more flexible than the 20

exponential model whi le retaining simplicity. 21

22

Accounting for Immunity 23

24

Whatever model you use , the risk of infection applies only to the susceptible 25

population. In fitting the model to a particular human pathogen

- challenge study data set, 26

it may be possible using available i nformation to take into account the fraction of 27

immune individuals, sometimes even if the participants were prescreened for presence of 28

antibodies to the organism. The general fitting algorithm for assessing the fraction of 29

immune individuals is given in Equation 4. 30

31

(1

– fr) * F(d, θ ) ( Eq. 4. 4) 32

33

where fr is the fraction of resistant (immune) individuals 27 , F is the dose

- response 34

function ( e.g., exponential, beta

- Poisson), d is the dose

, and θ is the parameter vector 35

associated with F. If the subjec ts were screened for prior exposure, you should offer an 36

explanation of the (unexpected) finding of a resistant fraction when using this model

. 37

Explanations could be based on theoretical considerations or experimental conditions 38

specific to the case

. 39

40

Det erministic Models 41

42

The foregoing hit

- theory models are stochastic in nature, primarily in that each 43

host may or may not become infected at any given pathogen dose. Models that are 44

27 Alternatively, 1

– fr can be replaced by fs, the fraction of susceptibles.Page 78 Microbia l Risk Assessment Guideline

deterministic in nature also have been proposed for microbial dose

- response evaluation 1

(Moon et al., 2005). These models are deterministic because they assume that each host 2

has a unique tolerance, or threshold dos e, above which infection is 100 percent certain, 3

similar to chemical dose

- response assessment. An advantage of thes e models is that i n 4

general, these models are more flexible than the hit

- theory models and will tend to fit 5

better to many data sets. A disadvantage is that they can result in over prediction of risk 6

at low doses (dilutions of single organisms)

, because t hey do not recognize the discrete 7

nature of pathogen distribution. Over prediction is likely to happen for dose

- response 8

data sets characterized by high variability, high response at lower doses

, or slowly 9

increasing responses across large dose ranges. O ver prediction is particularly prevalent 10

in modeling uncertainty in bootstrap simulations, for example. In addition, the biological 11

plausibility of individual (deterministic) host thresholds has not been established for 12

pathogens, as it has for chemicals. Therefore, at least for low

- dose extrapolation (e.g., 13

determination of unit infectivity), deterministic models are not recommended. However, 14

for high

- dose risk estimation, deterministic models can be useful because of their ability 15

to fit the response d ata better than the one

- hit models. 16

17

Bayesian Hierarchical Models 18

19

Bayesian methods to estimate dose

- response model parameters and evaluate their 20

uncertainty are also being used (Englehardt, 2004; Englehardt and Swartout, 2004; 21

Messner et al

., 2001). Th ese methods are particularly useful in cases where data are 22

available from multiple studies. One

- stage or hierarchical models can be fit to the data 23

using methods that include Markov Chain Monte Carlo Simulation (MCMC ) ( Gilks et al.

, 24

1996; FAO/WHO, 2003 ; EPA 1997b ). A Markov chain is a stochastic model having 25

discrete states in which the probability of being in any state at any time depends only on 26

the state at the previous time and on the probability transition matrix. M CMC 27

simulations can be used to ge nerate samples from the joint posterior distribution 28

(Messner et al., 2001). An advantage to these models, i n contrast with traditional 29

statistical techniques, is that they are able to exploit subjective and related information in 30

addition to numeric data

. 31

32

A predictive Bayesian dose

- response function can be developed as follows. First, 33

the parametric form of the dose

- response function is established by theoretical derivation 34

and, if possible, empirical confirmation. Then all available knowledge, other than the 35

theoretical form of the conditional distribution and empirical data already used for that 36

purpose, is brought to bear upon estimation of the parameters of the distribution. To do 37

this, the parameters are recognized as uncertain but subject to pr ofessional judgment, and 38

thus, a prior probability distribution is assigned to each parameter. Prior distributions are 39

then refined with dose

- response data, to obtain a posterior distribution. Next, the 40

predictive Bayesian dose

- response function can be f ound by multiplying the posterior by 41

the conditional dose

- response function, and integrating over the parameter space 42

(Englehardt

, 2004). Bootstrap methods (i.e., repetitive Monte Carlo sampling directly 43

from the data or from data summary distributions) w hether used in a Bayesian or 44

frequentist framework, may also be used to evaluate parameter uncertainty in dose

45

response models (Teunis et al., 1996; Haas et al., 1999; Englehardt and Swartout, 2006). 46Microbia l Risk Assessment Guideline Page 79

Draft

– Do not cite, circulate, or quote These models can be more complicated than other models described here, are generally 1

less familiar to scientists and managers, and can be difficult to explain. 2

3

Bayesian hierarchical models have also been used to develop dose

- response 4

relationships for pathogens based on outbreak data rather than feeding study data. For 5

example, Teunis et al. (2005) analyzed Campylobacter jejuni dose response using 6

Bayesian methods. Data from both a human volunteer study and an outbreak caused by 7

drinking raw milk were combined in this analysis. The model incorporated both t he 8

probability of infection and the conditional probability of illness given infection. First, 9

for the outbreak a certain probability of illness (p0) was assumed for those who were 10

unexposed to the raw milk but might have become ill due to an alternative route of 11

transmission. Second, a beta

- Poisson model was used to model the probability of 12

infection given a mean dose (D). Third, a model for the conditional probability of illness, 13

given that the individual is infected and had mean dose D, was developed. Non

14

informative prior distributions for the parameters were defined. The posterior mode 15

parameter values were calculated by directly maximizing the posterior probability. These 16

values were used to compute the posterior mode dose

- response functions for t he 17

probability of infection and the probability of illness given infection. Uncertainty 18

intervals for these dose

- response functions were computed by using MCMC to simulate 19

vectors of parameter values. Teunis et al. (2008a) also analyzed data from eight 20

o utbreaks of E. coli O157:H7 using a hierarchical Bayes model and used Bayesian 21

methods to analyze dose

- response functions for the Norwalk virus

, based on a volunteer 22

study (2008b). 23

24

4.2.2 What is the Output of a Dose

- Response Assessment? 25

26

The output of a dose

- response assessment is a value or a set of values for the dose 27

response parameters. For example, for the exponential dose

- response model (described 28

in section 4.2.1) a sin gle value (―r‖) would be required and for dose

- response models 29

having more than one parameter, a set of parameter values will be required. For many of 30

the most common dose response functions, the relationship between exposure and risk is 31

linear at low dos es (Haas et al.

, 1999) . For exposures to many organisms at once (such as 32

in food), the risk of infection needs to be calculated from the mathematical dose

- response 33

function itself. 34

35

Computing the risk of infection may be necessary to determine the popula tion at 36

risk for illness, but infection, in itself, is not necessarily adverse. Therefore, information 37

on the rate of illness, given infection, is needed to perform a meaningful risk assessment. 38

Although orally ingested pathogens can cause a number of sy mptoms, some severe 39

enough to be life

- threatening, the only endpoints examined in human studies (for ethical 40

reasons) are related to gastrointestinal illness , primarily diarrhea. The risk of illness is 41

estimated in a similar fashion as for infection, exce pt that the dose

- response model is 42

constrained such that an illness response is strictly conditional on infection. The 43

constraint is trivial for the one

- parameter exponential model, as the slope is fixed. For 44

models in which the slope can change (more th an one parameter), a higher risk of illness 45

than infection can be predicted for some data sets if the constraints are not strict. Illness 46Page 80 Microbia l Risk Assessment Guideline

without infection is not biologically plausible so constraints are necessary to avoid this 1

implausibility. Applying strict constraints in such models is not a trivial exercise, and is 2

a topic of ongoing research. The output of an illness dose

- response assessment generally 3

is a morbidity ratio, which is the fraction of those who are infected that become ill. A 4

common p ractice is to assume that the risk of illness is constant once an individual 5

becomes infected, no matter what the dose. In this case, the morbidity ratio is simply the 6

number of ill individuals divided by the number of infected individuals. Dose

- dependen t 7

morbidity ratios, where the conditional (on infection) risk of illness increases with 8

increasing dose, are more difficult to model, requiring the strictly constrained model 9

previously discussed (Refer to Teunis et al.

, 2005). For drinking water risk ass essments, 10

where the exposures are frequently very low, the occasionally large difference between 11

constant and dose

- dependent morbidity ratios can be highly significant. 12

13

You should be transparent in your discussion of the dose

- response output. For 14

exampl e the dose

- response is based on a defined health endpoint, a defined human 15

population (e.g., the population used in a clinical trial or an epidemiological study), and is 16

influenced by model selection. Be clear about all the constraining features of the da ta 17

and what assumptions were made when those data were used to extrapolate to broader 18

human populations or health endpoints. For example, the risk managers who will use the 19

risk assessment results to inform their decision making need to understand the 20

imp lications of dose

- response data based on healthy adults

, when their goal is protection 21

of the general population that includes all life stages and sensitive populations. 22

23

4.2.3 How do I Fit Models to Existing Dose

- Response Data? 24

25

A summary of dose

- response relationships published in the peer

- reviewed 26

literat u re for waterborne exposures is provided in Table 4.1. In many cases, you can 27

simply select the most appropriate dose respo nse relationship from one that has already 28

been peer reviewed. If an appropriate dose

- response relationship for the specific 29

pathogen/matrix combination is not available, you can derive a relationship in several 30

ways. If a valid dose

- response data set is available, you should be able to fit the data to a 31

mathematical dose

- response model. The dose

- response model provides a prediction of 32

the incidence of the effect in the population

, given a specific exposure level or dose. The 33

dose

- dependent probability distribution is defined by a mathematical equation with one 34

or more parameters. The best values for those parameters are determined by assessing 35

the likelihood of observing the data, given specific parameter values. The parameter 36

values that result in th e greatest likelihood of the data are chosen as the most 37

representative ones. The determination of the best parameter values can be done directly 38

on the data (as in a ―frequentist‖ approach)

, or by updating a prior judgment as to what 39

the parameters might be (a ―Bayesian‖ approach). In either case, the resulting fitted 40

dose

- response model is used for predicting human response, either infection or illness, to 41

pathogen exposure for a specific risk

- assessment scenario. 42

43

The typical dose

- response assessment is performed by a direct fit of the functional 44

form to the data using a ―frequentist‖ approach. That is, the fitted parameters represent 45

the frequency with which some event has happened previously. The fitted parameters, 46Microbia l Risk Assessment Guideline Page 81

Draft

– Do not cite, circulate, or quote however, are generally used to p redict the future occurrence of that event. Proponents of 1

Bayesian methods argue that a Bayesian approach is a virtual requirement when trying to 2

assess probabilities of future outcomes, rather than the frequencies of past events 3

(Bernardo and Smith, 1994 ; Berry, 1996; Carlin and Louis, 2001). Bayesian methods 4

provide a rigorous framework for common

- sense interpretation of statistical conclusions. 5

Frequentist approaches can only place confidence limits on a result that depends on 6

specific conditions, lea ding to inferences that might be made in repeated practice. 7

Bayesian proponents point out that most people erroneously interpret frequentist results 8

in the Bayesian sense (Gelman et al., 2004). Bayesian methods generally provide much 9

greater flexibility than do frequentist methods, allowing you to cope with very complex, 10

data

- limited problems. 11

12

Bayesian methods, however, are not without problems of their own. First, 13

computation of the more complex integrals generally involve s complex numerical 14

techniqu es, with which many practitioners will not be familiar. Most analysts will have 15

to rely on programs, which, in themselves, require some degree of mastery of functional 16

coding techniques. Second, Bayes theorem requires that a prior relationship between 17

do se and response be specified, which can be problematic for pathogens. The prior, 18

which is usually subjective, can vary among investigators. Non

- informative priors, 19

generally uniform distributions over a wide range of parameter values, address the 20

subject ivity issue but can make strong statements about prior belief of infectivity. An 21

example of the latter and, perhaps, the only truly non

- informative prior, would be the 22

simplest prior on the r parameter for the exponential distribution ( see S ection 4.2.1 ) of a 23

uniform distribution between 0 and 1 (the full range of r ). Although this prior says that 24

we really don‘t know anything a priori about infectivity, it establishes a prior expected 25

value of 0.5 for r , which is much greater than any known actual pathog en infectivity. As 26

the Bayesian posterior is a compromise between the prior and the data, a lot of data 27

would be required to move the answer towards a less extreme value. 28

29

Examples of the use of Bayesian techniques in the literature include a number of 30

analyses of C

. parvum (Teunis et al., 1999; Messner et al., 2001; Englehardt and 31

Swartout, 2004, 2006; EPA, 2006 a ). These analyses are largely hierarchical, assessing 32

the aggregate infectivity of several isolates of C. parvum treating each one as a distin ct 33

strain. The outputs of most of these analyses consist of distributions of uncertainty for 34

―hyperparam e ters‖ of C

. parvum infectivity across strains. Such an analysis was used in 35

support of the Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESW TR) 36

promulgated by EPA in 2006 (EPA, 2006 a ). One of the analyses did not assume that the 37

C. parvum isolates were different, evaluating the aggregate illness

- response data across 38

all studies (Englehardt and Swartout, 2006). This analysis and one other (En glehardt and 39

Swartout, 2004) used an unconditional Bayesian form, in which the posterior represented 40

the dose

- response function integrated over the entire range of parameter values. This 41

unconditional or ―predictive‖ form is independent of any pre

- selecte d confidence level 42

(e.g

. , 95 percent ) and can be considered to be the expected value under uncertainty. All 43

of the cited C. parvum assessments can be considered to be meta

- analyses of sorts, as 44

they incorporate data from more than one study into a single aggregate output. Meta

45

analysis generally allows for a weight

- of

- evidence approach, where a more objective 46Page 82 Microbia l Risk Assessment Guideline

review of data may occur. Although none of these analyses did so, in a meta

- analysis, 1

individual data sets can be assigned specific weights (other than unity) prior to 2

quantitative analysis. In this case, a Bayesian framework offers a rigorous approach in 3

which to evaluate such weightings. Bayesian model averaging is an technique that 4

formalizes this process (Hoeting et al., 1999). 5

6

4.2.4 How Can I Evaluate Uncertainty in Dose

- Response? 7

8

It is possible to characterize the precision with which the dose response curve has 9

been determined. The statistical confidence limits pri marily reflect variability, with some 10

(usually unquantifiable) contribution from un certainty. Therefore the statistical 11

confidence limits do not represent the full expression of uncertainty. For single hit 12

models some of the uncertainty can be shown by d etermining the confidence limits to the 13

parameters of the dose response curve and also the upper and lower envelope around the 14

dose response curve (Haas et al.

, 1999). A likelihood

- based approach can be used for this 15

purpose. This yields an m

- dimensional region, where m is the number of parameters in 16

the dose response model under evaluation (one for the exponential, two for the beta 17

Poisson, etc.). The computational details of this approach are beyond the scope of this 18

document, but interested assessors are referred to Chapter 7 of Haas et al. (1999). 19

20

In hierarchical Bayesian approaches, the output of the uncertainty analysis can be 21

in the form of distributions on the model parameters or ―hyperparameters,‖ such as the 22

mean of the fitted beta distribution (the average unit infectivity) for the beta

- Poisson 23

dose

- response model. A distribution of plausible bounds on the response at any dose can 24

then be generated to obtain, for example, 95% confidence limits on the response at a 25

given dose. 26

27

In the predict ive Bayesian models, the parameter uncertainty is integrated with the 28

response variability to obtain a single dose

- response curve without confidence bounds, 29

but whose shape depends on the amount of information available; the shape becomes 30

narrower (less un certain) as the amount of information increases (Englehardt, 2004), 31

generally resulting in a prediction of lower mean risk than when information is sparse. 32

Another technique is the parametric bootstrap analysis, in which the result of the initial 33

dose

- res ponse model fit to the data is assumed to be the true response relationship. The 34

responses at each dose are then regenerated repeatedly based on the respective fitted 35

response probabilities assuming an underlying response distribution (generally binomial)

. 36

A distribution of ―resampled‖ responses is obtained for each dose, from which bootstrap 37

confidence bounds on the entire dose

- response curve can be calculated. 38

39

4.2.5 What is Variability in Dose

- Response? 40

41

It is likely that all parameters in dose

- response have some variability. Dose

42

response involves the interaction of host and pathogen, so any factors associated with 43

either the host or the pathogen ha ve variability and the interaction it self is also variable. 44

The variability in factors presented in Section 4.1.3 may be quantitative or qualitative in 45

nature. Depending on the needs of the particular risk assessment, y ou may need to use 46Microbia l Risk Assessment Guideline Page 83

Draft

– Do not cite, circulate, or quote statistical techniques to characterize variability in : 1

2

a) Dose

- response relationships between isolates or strains 3

b) Host immune responses (both immunity and susceptibility) 4

c) Duration of host immune responses 5

d) Host characteristics that influence the dose

- response relationship 6

e) Health effects 7

4.2.6 How Can I Account for Life Stages and Subpopulations in Dose

8

Response Models? 9

10

Unlike a subpopulation, a life stage is something the whole population passes 11

through, such as childhood. At any given moment only part of the population is in 12

childhood, but referring to children as a subpopulation underestimates the importance of 13

childhood life stage s in the larger picture. EPA breaks childhood down into ten age 14

groups based on behavioral and physiological milestones (EPA, 2005 ; Section 3.7). 15

Some life stages, such as children and the elderly, are more sensitive to pathogens 16

because of host susceptibility and/or behavior patterns when compared to other life 17

stages, such as a dults. In chemical risk assessment, life stages and sensitive populations 18

are frequently taken into account by the application of uncertainty factors. The use of 19

uncertainty factors for pathogens, however, is somewhat problematic because of the 20

discrete nature of exposure. Unlike for chemicals, pathogen dose is usually expressed in 21

non

- reducible organism

- level units as an average, such that fractional doses represent 22

probabilities of exposure to single organisms. Thus, when considering low

- dose 23

exposure s, a dose of 10

- 4 for a chemical may be in units of milligrams or micrograms, 24

representing perhaps 10 18 molecules, while the same dose for a pathogen would represent 25

a 1 in 10,000 chance of ingesting a single organism for a given exposure. Depending on 26

re lative infectivity of the pathogen, the indiscriminate application of a fixed uncertainty 27

factor could result in an impossible risk (risk of infection greater than probability of 28

exposure) or an arbitrarily high risk inconsistent with the overall exposed p opulation. 29

Accounting for sensitive life stages and populations for a low

- dose exposure scenario 30

(e.g., drinking water) would require an estimate of the relative infectivity for that 31

subpopulation compared to the general population, as well as the fractio n of that 32

subpopulation with respect to the entire population. With this information, a new 33

subpopulation infectivity parameter could be calculated as a weighted average. For 34

example, simply stating that you believe that a particular life stage is 10 tim es more 35

susceptible than the healthy adult population represented by the experimental data is not 36

adequate, in itself. In this case, if the r

- value for the healthy adult population is greater 37

than 0.1, an impossible risk (> 1) is projected for the life st age. Each case is likely to 38

require a unique solution, with professional judgment playing a large role. For some 39

pathogens, the probability of infection or disease for sensitive life stages and populations 40

may not differ from the normal population, but t he severity of the disease outcome may 41

differ

. 42Page 84 Microbia l Risk Assessment Guideline

1

4.2. 7 C an I Use Uncertainty, Modifying, or Adjustment Factors in a 2

Microbial Dose

- Response Assessment? 3

4

There are no standard guidelines for the application of uncertainty, modifying, or 5

adjustment factors in MRA as are in chemical risk assessment. In chemical risk 6

assessments, uncertainty factors are usually applied as factors of 3 or 10 , and are applied 7

to empirically derived effect levels (e.g., no observed adverse effect level) to 8

accommodate for a lack of knowledge associated with interspecies extrapolation, high

- to 9

low

- dose extrapolation (i.e., effect to no

- effect), population varia tion (i.e., protection of 10

sensitive populations), and extrapolation across exposure durations (e.g., subchronic to 11

chronic ). The areas of uncertainty potentially most relevant to MRA are the interspecies, 12

sensitive life stages

, and sensitive population ex trapolations. As an example, for 13

interspecies extrapolation, there are sound allometric

- scaling principles relating the 14

uptake and whole

- body distribution of chemicals and strongly conserved mechanisms of 15

toxicity across mammalian species. There are no c orresponding agreed upon principles 16

for cross species pathogenicity relationships. Because many pathogens are highly 17

species

- specific or produce different effects in different species, and immune response 18

mechanisms can be highly variable across species, use of uncertainty, modifying or 19

adjustment factors to justify extrapolation is highly suspect. 20

21

For chemical risk assessment, all uncertainty factors are applied as a divisor of the 22

dose to obtain a quasi

- threshold exposure level. For MRA , the assess or would have to 23

pay attention to the absolute value of the computed risks so that implausible or 24

impossible risks would not arise (e.g., product of general population risk and uncertainty 25

factor greater than 1). In essence, a variable

- magnitude uncertain ty factor scheme would 26

be a likely result, with smaller uncertainty factors for larger average population risks. 27

Furthermore, to obtain an overall population risk (adjusted for sensitive life stages and 28

subpopulations), the assessor would have to know the proportion of the population that is 29

sensitive (for proper weighting of each subpopulation

- specific risk). An example of this 30

process can be found in a human population infection

- response assessment for C. 31

parvum , in which human pathogen

- challenge study data were combined probabilistically 32

with assumptions about the size of sensitive and resistant populations to obtain an 33

estimate of overall population response (Englehardt and Swartout, 2004). 34

35

Because of the foregoing considerations, it is not usual pra ctice in MRA to use 36

uncertainty or modifying factors in a manner similar to their use in chemical risk 37

assessment. An important distinction between uncertainty factors and adjustment factors 38

is that uncertainty factors account for unknown distributions of sensitivity and adjustment 39

factors account for known differences in response. C ase

- specific adjustment factors can 40

be employed if strong defensible evidence supports their use. As an example, 41

epidemiological data was used as a justification for the appl ication of adjustment factors 42

in the FDA/USDA/CDC risk assessment for L. monocytogenes in ready

- to

- eat foods. 43

Scaling factors were used to adjust mouse

- derived dose

- response curves so they were 44

applicable to humans (FDA/USDA/CDC, 2003). The size of the s caling factor was 45Microbia l Risk Assessment Guideline Page 85

Draft

– Do not cite, circulate, or quote determined by surveillance data reported to FoodNet 28 for the receptor populations 1

modeled in the risk assessment. Similarly, in the FDA risk assessment on Vibrio 2

parahaemolyticus, the dose

- response curve was adjusted to reflect CDC‘s ill ness 3

estimate. The adjustment factor represents the effect of the apparent differences between 4

the dose

- response observed in human volunteers under controlled conditions versus that 5

in the general population when exposure is associated with the oyster foo d matrix. 6

7

4.2. 8 What Modeling Methods are on the Horizon? 8

9

Physiologically

- Based Dose

- Response Models 10

11

New physiologically based models are being developed to begin to capture the 12

biologi cal complexity associated with dose

- response (Blaser and Kirschner, 1999, 2007)

. 13

Physiologic models begin with the development of conceptual models that break the 14

process from exposure to establishment of infection to the expression of illness into 15

compar tments. These compartments serve as ―steps‖ in the process, and the parameters 16

inside each step are captured in the model. Published research and/or new research 17

provide or will provide the values for each parameter. This is one of the strengths of the 18

risk analysis concept, the clear identification of data needs, which can then been 19

translated into research priorities

. 20

21

28 http://www.cdc. gov/FoodNet/Page 86 Microbia l Risk Assessment Guideline



### 5. EXPOSURE ASSESSMENT 1

2

The goal of exposure assessment in MRA is to determine the route, frequency, 3

duration, and magnitude (amount) of exposure t o a microbial hazard in a population. 4

5

Microbial agents may come from more than one source, may be transmitted via 6

multiple routes of exposure, and may be spread via secondary transmission. Moreov er, 7

these routes of exposure may be inter

- related. Exposure routes relevant to a given 8

microbial hazard are situation

- dependent and influenced by the inherent properties of the 9

microorganism and its potential host(s). An exposure source can originate fro m either 10

natural or anthropogenic events, activities, or locations that generate or release microbial 11

hazards. 12

13

A number of factors de fine microbial exposure including the sources and 14

pathways of exposure, the growth and/or decline in numbers of microorga nisms and 15

variable intake amounts among individuals. Often, some of the necessary data for a 16

microbial exposure assessment are either lacking (i.e., need to be extrapolated from data 17

developed for another purpose or limited data that are not representativ e) or altogether 18

non

- existent. Given that complete data and information are rarely available for microbial 19

exposure assessment, you may need to make simplifying assumptions. Such assumptions 20

result in uncertainty about exposure estimates. To support bet ter risk management 21

decisions, you need to objectively characterize uncertainty about exposure assessment 22

results. 23

24

This chapter provides general principles and practical guidance for conduct ing 25

exposure assessments for microbial hazards. Information is organized into five sections: 26

1) general co ncepts in exposure assessment (S ection 5.1); 2) deve loping an exposure 27

assessment (S ection 5.2); 3) analyzing results from a model ( S ection 5 .3 ); 4) 28

communication, review and valida tion of model results (S ection 5.4); and, 5) future 29

develo pments in exposure assessment (S ection 5.5). The chapter is also intended to 30

provide information useful to risk assessors, risk managers, decision

- makers, risk 31

communicators, stakeholders and the general public, and researchers. Other resources 32

that provide overviews of exposure assessment are those by the WHO/ FAO (2008), 33

European Commission Scientific Steering Committee (ECSSC, 2003), Hass (1999), Cox 34

(2006)

, and Vose (2008 ). 35

36

5.1 What are General Concepts in Exposure Assessmen t? 37

38

This section includes factors to take into account when conducting an exposure 39

assessment. It also discusses variability, uncertainty, deterministic and stochastic risk 40

assessme nt, and Monte Carlo analysis. 41

42

5.1.1 What is an Exposure Assessment? 43

44

An exposure assessment is the process of estimating or measuring the magnitude, 45

frequency, and duration of exposure to a microbi al hazard(s), along with the number and 46Microbia l Risk Assessment Guideline Page 87

Draft

– Do not cite, circulate, or quote characteristics of the person or population exposed. You can provide either a qualitative 1

or quantitative evaluation of exposure , however a quantitative evaluation is always 2

preferable if the data exist and a quanti tative risk assessment is needed . Ideally, an 3

exposure assessment describes the sources, pathways, routes, and the uncertainties about 4

exposures. 5

6

5.1.2 What is Exposure? 7

8

Exposure comprises the sources, mode, r oute, and extent of contact the host has 9

with the microbial hazard(s) of concern. H ow often a person is exposed is referred to as 10

frequency of exposure. How long a person is exposed to a microbial hazard is referred to 11

as the duration of exposure. 12

13

MRA is typically only con cerned with single event exposures. But reoccurring 14

exposures can be included, particularly if secondary transmission is being modeled. The 15

number of microorganisms that correspond to a single exposure is referred to as the dose. 16

T he exposure dose may also be the total number of organisms that constitute a set of 17

exposures. Simultaneous exposure to multiple hazards is also a concern in MRA. 18

19

5.1.3 What are Sources, Pathways, and Routes of Exposure? 20

21

There are various terms used to discuss the origin, movement or spread, and final 22

intake of microorga nisms by individuals or populations. Generally, the overall 23

terminology refers to routes of transmission fo r microorganisms. 24

25

The entity (or entities) that supply microorganisms to a particular exposure route 26

is the source. The source of microorganisms could be infected food animals, industrial 27

processes, the environment (water, air, soil) , or infected persons

. The route of exposure 28

(or route of intake) is where the microorganism comes into contact with the host. The 29

three common routes of exposure are oral, inhalation, and dermal. 30

31

The ph ysical movement of microorganisms , over time, from their source to the 32

occurrence of an exposure is referred to as the exposure pathway or route of transmission. 33

Exposure pathways may be complex; exposures may occur via ae r osolization , water, 34

food, soil, fecal

- oral and/or inanimate sources. The mode of transmission can be w ind, 35

flowing water, equi pment movement, or vector organism

. Not all modes of transmission 36

are relevant for all exposure pathways. For example, neither inhalation via the nose nor 37

dermal exposure are highly relevant for foodborne exposures. The number of 38

microorganisms in a particular medium can increase or decrease across time as a function 39

of changing environmental conditions

, throughout the exposure pathway. 40

41

Elements of Source Evaluation 42

43

While sources of microorganisms may be living or inanimate, t he elements of 44

source evaluation are basically the same , again with the caveat that not all modes of 45Page 88 Microbia l Risk Assessment Guideline

transmission are relevant for all exposure pathways

. In a farm to fork model you can 1

consider: 2

3

a) How many viable pathogen s (or indicators) are present at t he source (e.g. infected 4

chicken, contaminated carcass) at time zero? 5

b) How many are released from the source? 6

c) Over what time period are they released? 7

1) Continuously 8

2) Batchwise 9

d) At what rate are they released? 10

1) Counts/ unit time (e.g., cfu, pfu, genomes per minut es, seconds, hours, days) 11

e) What is the form of the release ? 12

1) Fomites 13

2) Spray equipment 14

3) Offgases from a fermentor 15

4) Waste water 16

5) Animal slaughter 17

f) To w h a t medium are the y released 18

1) Food 19

2) Surface water 20

3) Soil 21

4) Air 22

5) Other surfaces 23

24

The evaluation of movement from a source into an exposure pathway is 25

sometimes called a release assessment. The purpose of the environmental release 26

assessment is to identify the sources of potential release, the media of release (air, water, 27

or land) and the magnitude and frequency of release. The release estimates then serve as 28

inputs to the assessment of survival and distribution subsequent to release. 29

30

When there are data available to predict releases, four main steps are used in 31

constructing the release assessment. 32

33

a) First, you should co llect and synthesize information on how many organisms are 34

generated at the site of release. For example for land application of biosolids , this 35

would include the size of each load

, the number of loads per unit time , and the 36

concentration of organisms in a load

. 37

38Microbia l Risk Assessment Guideline Page 89

Draft

– Do not cite, circulate, or quote b) Second, develop a process description to locate the places where releases may 1

occur. For an industrial process one needs to know where and how the 2

microorganisms are grown and how they are separated from their growth medium. 3

The process descri ption should also consider the manner in which 4

microorganisms may be subjected to circumstances such that they may be 5

inactivated or destroyed. 6

7

c) Third, relate each possible point of release to the process involved. In agriculture 8

this could be a drop spr eader or spray nozzle. In an industrial plant it could be 9

off

- gassing (during separation, as from a centrifuge), from equipment during 10

clean

- up, or during product transportation. 11

12

d) Fourth, for each release source develop quantitative estimates of release which 13

specify the amount of release, the time frame of release

, and the media of release. 14

If inactivation procedures or engineering controls are applied to the release 15

source, then their effectiveness will need to be estimated to quantify the amount 16

relea sed after the control or treatment. 17

18

When intentional or incidental releases occur from inanimate sources, quantitative 19

estimates can frequently be obtained. Releases to air from sources such as a fermentor‘s 20

off

- gas can be measured as the viable c ount pe r site per unit time. You can treat these as 21

point source releases occurring at approximately rooftop height. A similar approach can 22

be employed for wastewater releases. Modeling of release modes is usually medium 23

specific; the output may be useful in f urther estimating dispersal of the microorganisms 24

from their source. Incidental releases may be modeled based on empiric evidence 25

compiled for specific activities, but source evaluations of intentional releases to the 26

environment are often complex and cas e specific

. 27

28

The dynamic nature of microorganisms is one characteristic that differentiates 29

microbial exposure assessment from chemical exposure assessment. Predicting changes 30

in the number of microorganisms along an exposure pathway is often necessary to 31

accurately estimate exposure doses. Environmental conditions that can influence the 32

growth and decline in the number of microorganisms present in a specific media include

, 33

but are not limited to : 34

35

a) water activity

, 36

37

b) pH

, 38

39

c) carbon source

, 40

41

d) electron acceptor

, 42

43

e) sunlight intensity

, 44

45

f) temperature

, 46Page 90 Microbia l Risk Assessment Guideline

1

g) population density of the microorganisms and/or other micro biota that compete 2

for nutrients in the media or support pathogen growth e.g. within biofilms on 3

surface s , and/or 4

5

h) presence of disinfectants or antimicrobials

. 6

7

Depending on the characteristics of the microorganisms, some survive throughout an 8

exposure pathway while others do not. 9

10

Many microorganisms have more than one exposure pathway and corresponding 11

route of intake, often referred to as an exposure route or route of entry. Common 12

exposure routes are: 13

14

a) inhalation (nose and mouth to lungs)

, 15

16

b) ingestion (oral intake of food, water, soil, and inanimate objects) , and 17

18

c) direct (via skin, eyes, ears, inanimate objects, and sexual contact)

. 19

20

Some microbial exposure a ssessments are able to characterize exposure via a 21

primary exposure pathway (e.g., Escherichia coli O157:H7 from infected cattle to an 22

individual via undercooked ground beef ). Nevertheless, microbial and epidemiological 23

evidence may indicate that some mic roorganisms spread via cross

- contamination 24

pathways; these pathways are sometimes difficult to characterize. Also, many 25

microorganisms (e.g., noroviruses) are spread from an infected person to another who is 26

not . This exposure pathway may be poorly chara cterized because sufficiently detailed 27

data are often lacking (Zhao, 1998). 28

29

In addition to ingestion routes, chemical risk assessment is well developed for 30

inhalation and dermal exposure routes. For example EPA has many guidance document 31

for chemicals su ch as, Methods for Derivation of Inhalation Reference Concentrations 32

and Application of Inhalation Dosimetry, the Exposure Factors Handbook

, and Risk 33

Assessment Guidance for Superfund Volume I: Human Health Evaluation Manual (Part 34

E, Supplemental Guidance for Dermal Risk Assessment) Final (RAGS

- E) (EPA, 1994, 35

1997a, 2004d)

. Inhalation and direct (e.g.

, dermal) exposure routes and to what degree 36

chemical guidance document might be adapted for MRA may be further discussed in 37

future volumes of this Guideline. 38

39

5.1.4 What Environmental Factors Can I Take into Consideration? 40

41

Environmental factors are considered when the risk assessment is at the stage of 42

calculating the amount o f pathogen that constitutes an exposure. Examples of 43

environmental factors are provided below with a short description. 44

45Microbia l Risk Assessment Guideline Page 91

Draft

– Do not cite, circulate, or quote a) Ecologic al N iche

– An ecologi cal niche is an n

- dimensional hyperspace 29 where 1

the abiotic and biotic elements in the environment allow a species to survive. As 2

an ecologic niche c hanges, the amount of pathogen might fluctuate and/or the 3

likelihood of exposure might increase or decrease. 4

5

b) Gradients of Concentrations

– It is rare that microorganisms are equally 6

distributed throughout a m edi um , therefore gradations of concentrations can be 7

considered in accordance to the media being assessed, and the exposure being 8

characterized. 9

10

c) Persistence

– Persistence in the environment of an organism (in some form i.e., 11

spores) can be considered. Those organisms that are not stable in the environment 12

would pose a different exposure, and subsequent risk, than those that are stable. 13

(See Chapter 3) 14

15

d) Matrix Characteristics

– The characteristics of the matrix where the pathogen is 16

found may determine t he amount and state (e.g., dormant, alive, dead) of 17

microorganism available to a receptor. Things like oxygen content, fat content, 18

pH, temperature, water content influence the survivability of microorganisms. 19

20

5.1.5 What is an Exposure Scenario? 21

22

An exposure scenario (or hazardous event) is the set of conditions or assumptions 23

about sources, exposure pathways, amounts or concentrations of microorganisms, and the 24

characteristics of the exposed individ ual, population, or subpopulation that constitute one 25

or more exposures. An exposure assessment may comprise many different scenarios. 26

Each scenario is the basis for evaluation and quantification of exposure(s) in a given 27

situation. 28

29

An analysis of ―what if‖ options for mitigation measures, interventions, or policy 30

changes is often referred t o as a scenario analysis. This type of analysis allows you to 31

evaluate the public or environmental health benefits of various measures that prevent or 32

mitigate expos ures. 33

34

5.1.6 What are Qualitative and Quantitative Exposure Assessments? 35

36

A qualitative exposure assessment is based on data and information which, when 37

considered along with expert knowledge and identification of attendant uncertainties, 38

provides a characterization of exposure in descriptive terms (e.g., high, medium, or low). 39

You will need to conduct qualitative exposure assessment when there is not sufficient 40

numerical data to develop a quantitative exposure assessment. 41

42

A quantitative exposure assessment provides numerical expressions of exposure. 43

Such an assessment provides numerical estimates of the likelihood of different microbial 44

29 N

- dimensional space that has more than 3 dimensions can be called hyperspace

.Page 92 Microbia l Risk Assessment Guideline

dose amounts, as well as numerica l measures of confidence about its estimates (i.e., 1

uncertainty). 2

3

Sometimes multiple quantitative exposure assessments are conducted for a 4

microbial hazard in order to rank sources, vehicles, and/or pathways of exposure based 5

on risk. One example of su ch an approach is a risk assessment of Listeria monocytogenes 6

in ready

- to

- eat foods ( FDA/ USDA/CDC , 2003). 7

8

5.1.7 What is Variability in Exposure Assessment ? 9

10

Variability describes a rang e of possible events. It is purely the effect of chance; 11

it can only be altered by changing the chance of something occurring. Variability in 12

exposure can be caused by differences in location, activity, and/or behavior of exposed 13

individuals at a particu lar point in time. These sources of variation result in differences 14

in exposure to a microbial hazard(s) in various media. Variability is also caused by 15

differences in the initial occurrence of microorganisms in various media (e.g., air, soil, 16

food

, and water). Because microorganisms grow and decline within media along the 17

exposure pathway, there is variability in the amount of microbial hazard per unit of media 18

intake by an individual or sub

- population. You may need to characterize variability in: 19

20

a) The number of microorganisms initially present in the medium 21

b) The environmental conditions in which microorganisms exist 22

c) The processes through which microorganisms move within scenarios 23

d) The dose of microorganisms per unit of intake (e.g., ser ving of food, inha lation 24

unit, amount of water ingested ; spatial and temporal variability) 25

e) The amount of intake (inter

- individual variability in exposure) 26

f) Exposures across time (temporal variability) 27

g) Exposures across geographic location (spati al variability) 28

The types of va riability considered depend on the type of exposure assessment to be 29

developed as part of the overall risk assessment. 30

31

5.1.8 What is Uncertainty in Exposure Assessment ? 32

33

Uncertainty is imperfect knowledge. You can reduce uncertainty by further study 34

or by accumulating more information. Uncertainty may reflect imperfect knowledge of 35

the microbial hazard (e.g., its virulence), environmental pathway/processes, or the human 36

populations und er consideration. Sources of uncertainty fit into two broad categories: 37

38

a) Uncertainty regarding one or more parameters in an exposure assessment 39

(parameter uncertainty) 40Microbia l Risk Assessment Guideline Page 93

Draft

– Do not cite, circulate, or quote 1

b) Uncertainty as a result of incomplete information or scientific theory needed to 2

fully define the causal bases of exposures (structural model uncertainty) 3

4

Availability and quality of data and information can reduce the amount of 5

uncertainty in exposure estimates. Objective depictions of uncertainty improve the 6

transparency of information used by decision

- makers in managing risk. The process of 7

interpreting the influence of uncertainty on the results of an exposure assessment is 8

referred to as u ncertainty analysis

. 9

10

5.1.9 What is a Deterministic Exposure Assessment? 11

12

You can conduct a quantitative exposure assessment using ―most likely‖ or 13

―conservative‖ values for the variables and uncertain parameters included in the set of 14

scenarios. However, unless data are not ava ilable, the use of single point estimates is not 15

a preferred approach to inform decision making (OMB, 2007b ; EPA, 2002b )

. These 16

values are often referred to as point estimates and can result from collapsing the 17

variability and/or uncertainty about random v ariables or parameters into singular values. 18

Depending on how the point estimates are designed, the results may either represent an 19

average or other extreme exposures (e.g.

, 95 th percentile) among a specified population. 20

21

The use of point estimates in a n exposure assessment is referred to as 22

deterministic (static) modeling. Point estimates do not account for variability in the 23

occurrence of the microorganisms at the source, variability in growth and/or decline in 24

the number of microorganisms through the exposure pathway, or variability in intake 25

across the population of individuals exposed to the microorganisms. Furthermore, a 26

deterministic exposure assessment does not explicitly characterize the uncertainty about 27

exposures. Without explicit characteri zation of variability and uncertainty, it is possible 28

that point estimates will substantially over

- or under

- estimate exposures. I f highly 29

conservative point estimates

– thought to be protective of public health

– are used, the 30

deterministic results may b e characterized as worst

- case estimates. 31

32

In some cases, deterministic modeling may be used to simplify the modeling of a 33

highly complex system. For example, extensive modeling of transmission processes 34

among a population may require simplifying assumpt ions about contact rates and 35

transmission coefficients. 36

37

Another use of deterministic modeling is during your initial analysis of an 38

exposure assessment model. Propagating simple numbers through the model may help 39

with error

- checking the mathematics of the model. Also, such calculations can provide 40

early indications of the importance of various model components or pathways. 41

Nevertheless, conclusions from such screening analyses should be cautiously interpreted 42

because omission of variability can genera te misleading results. 43

44

45Page 94 Microbia l Risk Assessment Guideline

5.1.10 What is a Stochastic Exposure Assessment? 1

2

In contrast to using point estimates, the use of probability distributions for each 3

param e ter in an exposure a ssessment is preferred . A probability distribution includes 4

both a range of values and the likelihood of occurrence for each numerical value. Use of 5

probability distributions throughout the exposure assessment allows you to represent 6

variability in expos ures of individuals and/or sub

- populations. You can also use 7

probability distributions to characterize the uncertainty in exposure assessments. When 8

developing a stochastic model, you can use point estimates to verify the mathematical 9

formula or confirm that the computer code actually performs correctly for trivial cases 10

( for example, the microbes are not present in the output if the initial concentration is 11

zero)

. 12

13

The use of probability distributions in an exposure assessment is referred to as 14

stochasti c modeling. Probability distributions represent the variability and uncertainty 15

inherent in a system. Stochastic modeling can provide more realistic results by 16

accurately characterizing the impacts of known sources of variability and uncertainty on 17

risk estimates . Risk assessments are often concerned with the occurrence of rare events 18

and stochastic modeling may reveal rare but consequential result s (e.g., the occurrence of 19

an unlikely, but large, population outbreak

. ) 20

21

Stochastic models often use compu ter simulations to mathematically combine 22

multiple probability distributions in an exposure assessment calculation. Monte Carlo 23

analysis (more detail in next section) is the most widely used probabilistic method to 24

estimate these combinations. Advanced M onte Carlo modeling techniques can also 25

quantitatively characterize uncertainty in exposure estimates (Gilks et al., 1996). 26

27

Stochastic modeling is usually more resource

- intensive than deterministic 28

analysis. Defining model inputs as probability distribut ions can require additional steps in 29

the planning, review, and communication of the exposure estimates. Use of probability 30

distributions, however, provides a framework for incorporating more of the available 31

information into an exposure assessment. 32

33

If da ta for critical variables and parameters are available, then you should 34

consider stochastic modeling for the exposure assessment if time permits. Alternatively, 35

if data and information are insufficient, consideration should be given to the use of other 36

mo deling techniques. For example, the use of interval mathematics or fuzzy mathematics 37

may provide a more credible assessment of the probabilistic boundaries of exposure than 38

standard methods using probability distributions (Ferson, 1996). 39

40

5.1.11 What is M onte Carlo Analysis? 41

42

Monte Carlo analysis is a commonly used quantitative technique for exposure 43

assessments. It involves the random sampling of each of the probability distributions in a 44

model to estimate the likelihood of the mode l‘s possible results (Vose, 2008 ). Each re

- 45Microbia l Risk Assessment Guideline Page 95

Draft

– Do not cite, circulate, or quote calculation of the model is termed an iteration, and a set of iterations constitutes a 1

simulation. 2

3

A cardinal rule of this analytic al technique is that every iteration should b e 4

possible in nature (Vose, 2008 ). If followed, this rule can prevent errors in modeling 5

logic. For example, a predicted serving cannot contain 2.7 microorganisms, although the 6

average concentration across some volume or mass may be 2.7 microorganisms. While it 7

is not possible that any individual would be exposed to exactly 2.7 (viable) organisms, 8

the simulation can be acceptable if the exposure distribution is defined and sampled 9

correctly. If a Poisson (discrete) distribution with a mean of 2.7 is use d in the risk 10

modeling, the individual Monte Carlo iterations will all take discrete (integer) values, 11

maintaining the realism of each iteration and of the simulation as a whole. 12

13

You may face three common problems when using Monte Carlo methods: 14

15

a) First, correlations and dependencies between variables may be unknown. If 16

dependent variables are mistakenly assumed to be independent in a Monte Carlo 17

analysis, the likelihood of common occurrences in the real world may not be 18

correctly estimated via simulation

. If information on the correlations is not 19

available, then alternative methods (e.g., interval or fuzzy mathematics) may 20

avoid such mistakes because the probability boundaries calculated by these 21

methods can include a full range of correlations in their r esults (Ferson, 1996). 22

23

b) Second, the data necessary to estimate input distributions may be incomplete or 24

entirely lacking. Although inadequate data are a problem for any exposure 25

assessment method, Monte Carlo methods are particularly disadvantaged because 26

these methods require explicit definition of the model inputs. Sometimes uniform 27

or triangular distributions are used when data are sparse. 28

29

c) Third, the mathematical structure of the exposure assessment model may be 30

questionable. Risk analysts often ackn owledge the limitations induced by these 31

problems and employ sensitivity analysis (or other methods) to assess their 32

influence on estimated exposures ( Law and Keaton, 2000; Vose, 2008 ). Care 33

should be taken to make sure that uncertainties related to model specification are 34

addressed by comparing the quality of fits across different model forms. Where 35

sufficient data are available, methods such as cross

- validation may be used. 36

Using this approach, an exposure model is estimated using a portion of the data 37

(usually about 70 percent) and then the model is tested for consistency with the 38

remaining data set. 39

40

Examples of Agency g uidance on probabilistic risk assessment include, EPA‘s 41

Guiding Principles for Monte Carlo Analysis (EPA

, 1997b) and EPA‘s Using 42

P robabilistic Methods to Enhance the Role of Risk Analysis in Decision

- Making With 43

Case Study Examples (EPA

, 2009 b ). 44

45Page 96 Microbia l Risk Assessment Guideline

5.1.12 How does Exposure Assessment Fit with the Other Components of 1

Risk Assessment? 2

3

Fundamentally, risk assessment is a predictive analysis. It intends to ―envision 4

how the future will turn out if we undertake a course of action...‖ (Kaplan and Garrick, 5

1981). 6

7

Predictions are accompl ished by answering three questions: 8

9

a) What can go wrong? 10

11

b) How likely is that to happen? 12

13

c) What are the consequences given that it does happen? 14

15

The set of answers to the first question outline s the mutually exclusive scenarios 16

to be considered in an exposur e assessment. These scenarios are given the symbol s i 17

where the index i implies there are potentially many, mutually exclusive, scenarios 18

wherein something goes wrong ( i = 1 to N ). Each s i that is identified has an associated 19

probability that it occurs ( l i ), as well as some measurement of its consequence ( x i ). 20

21

To a large extent, planning and scoping and hazard identification will determine 22

the scenarios to consider for the exposure assessment. It is imperative that hazard 23

identification establish the bi ologic plausibility of causative mechanisms considered in 24

any exposure assessment. 25

26

Exposure assessment, therefore, is most invested in determining the likelihood of 27

scenarios and a provisional consequence of the scenarios. In the context of exposure 28

asse ssment, the provisional consequence is the magnitude of human exposure (usually 29

dose) that occurs at the end of the scenario. 30

31

The output of the exposure assessment is commonly combined with the dose

32

response relationship developed in hazard characterizat ion to predict the probability of an 33

adverse human health outcome. This combination is considered in the risk 34

characterization chapter (Chapter 6)

. 35

36

You can order the scenario triplets from smallest to largest microbial dose (i.e., x 1 37

≤ x 2 ≤

... ≤ x N ) and s imilarly align the scenarios and likelihoods for each of those doses. 38

This organization conveys the likelihood of increasingly larger microbial doses, as well 39

as the scenarios that are responsible for those doses. Likelihood is a number constrained 40

betwe en zero and one. Because the s 1 , s 2 ,... s N are defined as mutually exclusive 30 41

scenarios, it is also true that 42

43

30 Note that each scenario may include exposures through more than one pathway. Each scenario includes

defined combinations of exposures that are sufficiently d ifferent to justify separate consideration.Microbia l Risk Assessment Guideline Page 97

Draft

– Do not cite, circulate, or quote (Eq. 5.1) 1

2

(if all s i represent an exhaustive list of the things that can happen, then this sum will 3

exactly equal one). 4

5

In some exposure assessments, l i will refer to the fraction of all possible 6

exposures

– including exposures with a dose of zero

– that derive from scenario s i and 7

generate a microbial dose of x i . If you know the total exposures for the microbial hazard 8

that can occur in o ne year ( M ), then the frequency of microbial dose x i is F( x i ) = M l i 9

(i.e., the number of exposures with a dose of x i in one year is the product of total 10

exposures and likelihood). 11

12

Given the frequency of microbial doses, the exposure assessment can directly 13

generate a frequency distribution for human exposures (Figure 5

. 1). This distribution is 14

simply a graphic display of the paired { x i ,F( x i ) } values

. 15

16

17

18

Figure 5. 1 An illustrative exposure distribution developed by ordering scenarios ( s i ) 19

by the size of dose ( x i ) and calculating frequency as the product of total exposures 20

(M) and likelihood of the scenario ( l i ). 21

22

5.1.13 Do Different Exposure Scenarios Always Generate Different 23

Microbi al Doses? 24

25

Although exposure scenarios may be mutually exclusive (i.e., represent unique 26

pathways for microbial exposure), it is not necessary for different scenarios to result in 27

different magnitudes of exposures. For example, you might consider consumption of 28

water from a single source to which a treatment, with variable effectiveness, is applied. 29

One scenario might reflect highly effective water treatment and consumption of a single 30

12 ounce serving that contai ns no microorganisms. Another scenario might reflect less 31

effective water treatment and consumption of a serving of 6 ounces that contains no 32Page 98 Microbia l Risk Assessment Guideline

microorganisms. Although the scenarios are different, the resulting dose (0 1

microorganisms) is the same. 2

3

The process of developing an exposure assessment can be complicated and it is 4

not always understood, before the analysis begins, what scenarios will generate what 5

doses. It is possible to design the exposure assessment such that scenarios are grouped by 6

the d ose they generate. Alternatively, the scenarios can be described a priori and the 7

exposure doses they generate subsequently determined analytically. Depending on how 8

scenarios are defined, it is possible that one exposure scenario may be associated with 9

multiple doses (and their attendant likelihoods). 10

11

Consider a scenario wherein a hamburger patty

– initially contaminated with 100 12

E. coli O157:H7 organisms and stored at 60  ̊F for 24 hours, then cooked at 130  ̊F for 4 13

minutes

– is consumed by a healthy 30

- year old male. The likelihood of this scenario 14

might be 0.00089 percent of all servings of ground beef consumed by such a person. The 15

consequence, from an exposure perspec tive, might be that 1000 E. coli O157:H7 16

organisms are ingested. This scenario, when accompanied by all other scenarios for 17

hamburger patties, is useful because it provides sufficient detail for decision

- makers to 18

appreciate the risk of adverse human heal th outcomes from consuming hamburger patties 19

(especially once the risk characterization is completed). Ultimately, the purpose of risk 20

assessment is to support decisions regarding risk and those decisions typically hinge on 21

the three elements contained he re: 22

23

a) What are the substantial scenarios? 24

25

b) What are the magnitudes of their consequences? 26

27

c) How likely are such scenarios to occur? 28

29

Short contemplation of this example, however, will undoubtedly raise many 30

questions. How was the scenario identified; surely there are thousands of scenarios at 31

that level of detail (e.g., what about a hamburger that is cooked at 129 degrees Fahrenheit 32

[ ° F] for 2.5 minutes)? How was the likelihood determined? That likelihood seems very 33

precise; do all servings of hamburger fr om this scenario have the same likelihood of 34

occurrence? Why does an exposure of 1,000 microorganisms result from this scenario? 35

Do all hamburgers handled this way have the same number of microorganisms at 36

consumption? Are you really this certain about all of this? Are there parts of the 37

scenario that are more influential on the consequence than other parts? 38

39

The following sections of this chapter intend to outline general answers to these 40

questions by discussing the process of developing, analyzing, and reporting an exposure 41

assessment. These sections address scenario development, calculation of the likelihoods 42

of scenarios, predictions of exposure doses, uncertainty analysis, and interpretation of 43

exposure assessment results. Nevertheless, the idea s and concepts in these sections are 44

not intended to be dogmatic prescriptions for completing an exposure assessment. In 45

exposure assessment, there are many different approaches that may be valid for solving a 46Microbia l Risk Assessment Guideline Page 99

Draft

– Do not cite, circulate, or quote problem. Yet, all valid approaches share som e fundamental similarities. It is these 1

similarities that are the focus in the remainder of this chapter. 2

3

The concepts of scenario, likelihood, and consequence are fundamental to 4

developing an exposure assessment. When analyzing an exposure assessment, the 5

concepts of variability and uncertainty are crucial. Finally, effective communication and 6

validation are essential considerations when reporting the findings of an exposure 7

assessment. 8

9

5.2 How do I Develop an Exposure Assessment? 10

11

As discussed in Chapter s 1 and 2

, the beginning of the exposure assessment starts 12

in planning and scoping

. This important groundwork lays the foundation for a successful 13

exposure assessment. 14

15

In general, the exposure assessment should be parsimonious, that is, as simple as 16

possible while still including the important sources and steps leading to the exposure of 17

concern. Based on ―problem formulation‖ (see S ection 2.1.1), you should make 18

decisions regarding th e approach to exposure assessment ( e.g., attribution modeling or 19

process modeling , empirical modeling of epidemiological data

, probabilistic or 20

deter ministic, dynamic or static ) and structure of the assessment model (which pathways, 21

single or multiple mode ls) (Hurd and Kaneene, 1993). 22

23

Given the complexity of many exposure assessments, the process becomes a 24

multi

- disciplinary collaborative effort. Subject matter experts are regularly consulted and 25

their judgment incorporated into the process. 26

27

Conceivin g an exposure assessment can be daunting. For this reason, some 28

structure is needed. It is useful to begin by describing a conceptual model with all the 29

necessary scenarios, followed by a full mathematical development of the conceptual 30

model and, finally , by collection and analysis of data necessary to inform the model 31

inputs identified in the mathematical model. 31 This structure theoretically ensures 32

adequate attention to, and scrutiny of, the exposure assessment in an ordered and efficient 33

manner. 34

35

Effi cient exposure assessment is enhanced when precedents exist and are used. 36

For example, beginning with already published conceptual models that require minor 37

modification for a new application avoids unnecessary duplication of effort. Following 38

reasonable precedents is also how standard methods can evolve. 39

40

5.2.1 What is the Purpose of the Risk Assessment? 41

42

With respect to purpose, most risk assessments can be categorized into two broad 43

categories

– retrospective or prospective. A retrospective purpose applies to microbial 44

31 Note that this conceptual model may be software driven and is different than the conceptual model that is

developed in planning and scoping which illustrates the broad overview of how risk happens.Page 10 0 Microbia l Risk Assessment Guideline

hazards that are well

- established as occurring sporadically or epidemically. A 1

prospective purpose for a risk assessment applies to potential microbial hazards for wh ich 2

the adverse human health effects are not established. This categorization scheme is 3

important because a different series of questions should be considered prior to developing 4

the exposure assessment depending on the purpose category ( see Table 5

. 1). These 5

questions are not exhaustive, but the answers will guide the development of an exposure 6

assessment appropriate for informing specific risk management decisions. In addition, 7

answering these questions may also identify information requirements and me thods for 8

collecting this information. 9

10

Table 5

. 1 General Questions Considered Prior to Conducting an Exposure 11

Assessment 12

13

Prospective Purpose Retrospective Purpose

Is disease onset only a potential at this point,

with the luxury of time to provide an an swer? Is disease onset imminent or already occurring,

thus requiring an immediate answer?

Should the exposure assessment be structured

as an in

- depth analysis using less conservative

assumptions? Should the exposure assessment be structured

more as a scre ening analysis, using default

and/or more conservative assumptions?

Should the evaluation focus on both long

- term

and short

- term exposures? Should the evaluation focus only on short

- term

exposures?

Should the analysis focus on both low

- level

and high

- lev el exposures? Should the analysis focus only on high

- level

exposures?

Should attempts be made to measure (sample)

or model exposures within the body? Should concentrations be measured (sampled)

or modeled in the media of concern?

Should the evaluation co nsider all potential

exposure pathways for that particular microbial

agent? Should the evaluation focus only on those

exposure pathways of imminent concern?

Should the analysis attempt to consider

aggregate and/or cumulative exposures to

multiple microbia l agents? Should the analysis focus only on the microbial

agent causing the adverse health impact and

only this exposure?

14

5.2.2 Which Scenarios Can I Consider? 15

16

Scenario development is the concept ual and creative part of exposure modeling. 17

It melds considerations of purpose and scope with established or putative causal 18

pathways. Although standardized frameworks for some microbial exposure assessments 19

have been suggested (e.g., for food safety a pp lications, see Nauta, 2002), there are few 20

hard and fast rules for how or what scenarios should be considered in an exposure 21

assessment. 22

23

Despite the lack of standard scenarios, those included in a specific exposure 24

assessment should be clearly communica ted and understood. Explicit diagrams (e.g., 25

conceptual model) can be developed early in a project and these diagrams can include 26

detailed descriptions of the inputs, parameters, flow and relationships of these 27

components in the exposure assessment. It i s useful to establish meaningful symbols 28

within the diagrams to represent these model components early in the project. Consistent 29Microbia l Risk Assessment Guideline Page 101

Draft

– Do not cite, circulate, or quote use of symbols will encourage clear communication among risk assessors and risk 1

managers. 2

3

Detailed diagrams with consisten t symbols can be discussed with risk managers 4

early and often in an exposure assessment project. This approach encourages open and 5

clear communication of the modeling approach. Exposure assessment is an iterative and 6

collaborative process; clear descript ions of the model with constructive feedback from 7

persons outside the project will facilitate improvements at the conceptual model stage. 8

9

Exposure assessments should be parsimonious (simple as possible, but not 10

simpler). Although hazard identification ma y suggest that the chain of causation for 11

exposure is complex, the exposure assessment should only incorporate the complexity 12

needed for the purpose s of the risk assessment. In other words, risk assessments should 13

leave avenues unexplored that, although i nteresting, do not specifically address risk 14

management options. For example , the exploration of unproven causal relationships is 15

rarely useful in exposure assessments. 16

17

Conceptually, an exposure assessment begin s by considering the occurrence of 18

the tar get microorganism at some place and/or time (i.e., its source). It is plausible that 19

an exposure assessment could begin by characterizing the distribution of doses (or 20

concentration of microorganisms) at the time of exposure. Such a beginning would not 21

i nclude consideration of sources and processes that produced the exposures. 22

Nevertheless, if the purpose of the risk assessment was investigative and risk managers 23

strictly sought estimates of the potential adverse human health events that could occur, 24

the n this approach would be satisfactory. Of course, it would only work if data are 25

available to estimate the distribution of doses just prior to exposure. 26

27

Often exposure assessments begin by considering the occurrence of 28

microorganisms at a place and tim e that is somewhat distant and prior to the actual 29

human exposures. Availability of microbial occurrence data is one justification for where 30

and when to begin the conceptual model. For example, if the only microbial data 31

available refers to its occurrenc e prior to the application of some treatment process, then 32

the exposure assessment may begin at that place and time. The purpose of the risk 33

assessment is another justification for where and when to begin the conceptual model. 34

For example, if the purpose is to set a regulatory performance measure for some 35

treatment process, then the exposure model will need to begin its considerations of 36

microbial occurrence at some point prior to the treatment process. 37

38

Once the beginning of the exposure assessment is d etermined, you can use 39

predictive microbiology to determine how microbial occurrence changes before a dose 40

reaches an exposed human. Processes refer to events or phenomena that influence 41

microbial occurrence between the beginning and end of the exposure a ssessment. As 42

discussed later, generic processes include growth, attenuation, mixing and partitioning. 43

Which and how many of these processes are included in an exposure assessment depends 44

on the planning and scoping and hazard identification stages of th e project. 45

46Page 102 Microbia l Risk Assessment Guideline

Many microbial exposure assessments involving food or water will include three 1

general sequential stages: bulk processing, bulk tr ansport, and consumption (Haas et al., 2

1999). Including these stages is often necessary to examine the influence of factors (or 3

covariates) on microbial levels at the point of exposure. This inclusion is especially 4

important when the purpose of the risk assessment is to predict changes in risk from one 5

or more changes in these stages. For example, a risk assessmen t that examines a policy 6

to require more controlled refrigeration of a food after it is produced

– to limit growth of 7

the target microbe

– will include the bulk transport stage in the exposure assessment so 8

that the effect of the policy relative to current conditions is measurable. 9

10

To complete scenario development, you should determin e how each exposed 11

population will come in contact with the microorganism of concern. The three well

12

recognized routes of initial exposure are inhalation, ingestion, and di rect contact. These 13

routes will influence the mathematical scale of the model. For example, inhalation 14

exposures will likely depend on concentration measures of microorganisms (e.g., 15

microorganisms per cubic meter) while ingestion exposures may depend on tracking 16

actual counts of microorganisms to determine the average number of microorganisms in a 17

serving. 18

19

Three examples i llustrate different approaches t o developing exposure scenarios: 20

21

a) To estimate the risk of viruses in water, an exposure assessment considered the 22

volume of water consumed and the average concentration of viruses per liter of 23

water supplying a large city (Haas et al., 1993). In this exposure assessment, no 24

attention was given to mechanisms potentially responsible for the average virus 25

concentration or to factors that might cause variability in virus concentration 26

across time or water supplies. 27

28

For this example, a scenario could represent one possible combination of 29

conc entration (e.g., viruses per milliliter ) and water consumed (e.g ., milliliters per 30

person per day). Therefore, the set of scenarios would include all possible 31

combinations. Alternatively, this analysis is an example as a single scenario (e.g., 32

water consumed) with variable average dose of virus per day. 33

34

b) To estimate the risk of tuberculosis (TB) transmission on a commercial airliner, 35

an exposure assessment examined the spatial variability in concentration of this 36

mycobacterium (Ko et al., 2004). This assessment included air transfer rates 37

between different cabins in the plane, respiration rates of potentially exposed 38

individuals, distance from an infectious source, and the rate of expired TB 39

organisms from an infectious source. Hazard identification had indicated that all 40

these factors might influence exposures of pa ssengers to TB on a plane. The 41

exposure assessment focused on the cumulative exposure to infectious TB during 42

a long (> 8 hours) intercontinental flight. For this example, scenarios reflected 43

the combinations of cabin location of exposed individuals, loc ation of source 44

individual and airflow direction. 45

46Microbia l Risk Assessment Guideline Page 103

Draft

– Do not cite, circulate, or quote c) To estimate the risk of human illness from E. coli O157:H7 in ground beef, an 1

exposure assessment included causative factors during on

- farm production, 2

slaughter, and storage/preparation stages (Ebel et al., 2003). This complex farm

3

to

- table exposure assessment examined the influence of season, live animal 4

prevalence, transport, dehiding, carcass decontamination, carcass chilling, carcass 5

fabrication, grinding, storage/handling, cooking and consumption o n the predicted 6

exposure per ground beef serving. Hazard identification indicated these factors 7

might influence exposures. Control of many of these factors was considered by 8

risk managers. 9

10

For this example, scenarios reflected the combinations of differ ent sources (e.g., 11

prevalence of different classes of infected cattle presented for slaughter by 12

season) with effectiveness of decontamination procedures with 13

times/temperatures of storage and cooking. The unique combinations that 14

represent individual sce narios were too numerous to count. Furthermore, ground 15

beef servings were created from combinations of scenarios that produced the beef 16

that went into a load of ground product. Therefore, the exposure assessment did 17

not list distinct scenarios but, inste ad, produced frequency distributions for levels 18

of E. coli O157:H7 per serving of ground beef for low

- and high

- prevalence 19

seasons of the year. 20

21

An exposure assessment depends on the microbial agent's properties and the 22

environmental transmission factors relevant to exposures. Microbial agents may stem 23

from more than one source, may be transmitted via multiple routes of exposure, and may 24

be spread via secondary or vector transmission. An exposure source can originate from 25

either natural or anthropogenic events, activities, or locations that generate or release 26

hazards. Exposure sources can be classified as point sources or non

- point sources. 27

Exposure routes include inhalation (nose and mouth to lungs), ingestion (oral), and direct 28

contact (skin, eyes, ears, and sexual). Exposure routes are situation

- dependent and 29

medium specific. An exposure pathway encompasses both exposure source and route, 30

and generally is described by a source and release from a source, an exposure point, and 31

an exposure route. T able 5

. 2 illustrates the exposure assessment components and their 32

relationship to various exposure points. 33

34Page 104 Microbia l Risk Assessment Guideline

1

Table 5

. 2 Transmission Pathways for Microbi al Hazards 2

Source Release Exposure Point Exposure Route

Natural or

Anthropogenic

Point or Non

point N atural

Accidental

Intentional Food Meat

Poultry

Eggs and Egg

Products

Fish and shellfish

Produce

Dairy Products

Other Food

Products Ingestion

Direct

Contact

Water Surface Water

( Drinking )

Ground Water

( Drinking ) Ingestion

Direct

Contact

Inhalation

Recreational

Water

Compost Tea 32

Incidental

Ingestion

Direct

Contact

Inhalation Soil Surface Soil

Subsurface Soil

Sediment

Manure

Biosolids

Air Ambient Air

Indoor Air

Surfaces Porous

Non

- porous

Biota Plants

Animals,

including

humans

3

The d ynamic nature of host

- pathogen interactions, unique to infectious disease 4

risk assessment, can lead to secondary transmission. The strictest definition of secondary 5

transm ission pertains to direct human

- to

- human contact between a primary case (infected 6

or ill) and a secondary case that becomes infected or ill from that contact. Broader 7

definitions include secondary cases that arise from contact with fomites or food or water 8

contaminated by primary cases. Where secondary transmission includes infection fr om 9

pathogens in the environment (e.g., fomites), it would not be considered secondary unless 10

32 Compost t ea is made by soaking or steeping compost in water. The resulting compost tea is used for

fertilizing plants.Microbia l Risk Assessment Guideline Page 105

Draft

– Do not cite, circulate, or quote it occurs in the context of an outbreak where primary cases have already been identified. 1

In such a case, the term ―secondary transmission‖ is not used in the str ictest sense, but is 2

commonly used by public health professionals in the context of an outbreak. 3

4

5.2.3 What are the Exposed Populations I Could Consider? 5

6

Identifying the exposed individuals or subpopulations of interest is crucial to 7

determining what data are needed for an exposure assessment. In some cases there may 8

be statutory requirements or Agency policy that require certain populations to be 9

considered. The following factor s are inherently tied to the exposure scenario: 10

11

a) Exposure Duration a nd Frequency

– Certain individuals/populations may have 12

a comparatively greater exposure duration or frequency in a given environment. 13

For some microorganisms, it is possible that exposur e to a low concentration for a 14

long duration may be a concern even if the concentration would not pose any 15

health risks under short

- term exposures. 16

17

b) Exposure Routes

– Knowing the characteristics of the exposed population helps 18

develop the appropriate expos ure pathways to consider in the exposure 19

assessment. 20

21

c) Sensitive Individuals/ P opulations a nd Life Stages

– Some 22

individuals/ sub populations may be more susceptible to infection or more likely to 23

develop severe manifestations of infection. For example, while healthy 24

individuals may recover from an E. coli infection, it can be deadly to young 25

children, the elderly, and people with compromised immune systems. Similarly, 26

chronic smokers may have impaired mucociliary clearance mechanisms, and 27

therefore, may be m ore susceptible to respiratory infections. 28

29

Once the exposed individual/ sub population or life stage has been determined, it is 30

important to include any scenario

- specific conditions in the conceptual model. Sensitive 31

p opulations and/or life stages which ma y have different exposure considerations include: 32

33

a) Young children (up to 10 different age groups (EPA, 2005) 34

35

b) The elderly 36

37

c) Persons with compromised immune systems 38

39

d) Pregnant women 40

41

e) Chronic smokers 42

43

f) Military personnel (deployed and non

- deployed) 44

45

g) Occupatio nally exposed individuals 46Page 106 Microbia l Risk Assessment Guideline

1

h) Other groups based on behavioral patterns (e.g. subsistence fishing ) 2

3

This list is not comprehensive

. You should select individuals/ sub populations of 4

interest based on the purpose of the risk assessment. However, your groupings may be 5

limited by data availability. Any differences in grouping between the sub populations 6

considered in the dose

- response and the exposure components of risk assessment should 7

be carefully explained and the implications of those differences should be d iscussed. 8

9

5.2.4 What are Common Approaches to Exposure Modeling I Can Use? 10

11

Although there are more elaborate classification schemes (Hurd and Kaneene, 12

1993), most exposu re assessments use an attribution and/or process modeling approach 13

(Cox, 2006). Process modeling is the more common approach and it generates more 14

traditional results (i.e., likelihood and dose) from the exposure assessment. Attribution 15

modeling, in cont rast, is used less frequently and does not explicitly estimate the 16

likelihood of microbial doses. Instead, this approach implicitly synthesizes the exposure 17

output with hazard characterization to generate links between model inputs and numbers 18

of human il lnesses. Some refer to attribution and/or empirically

- based models as  ̳black

19

box‘ approaches because causative mechanisms are not explicitly included (ECSSC, 20

2003). Nevertheless, attribution modeling is empirically based and is often available to 21

assesso rs when the problem is rich with surveillance evidence. 22

23

Attribution M odeling 24

25

Empirical estimates of the number of human illnesses per year caused by the 26

microorganism are sometimes available. These data might be used, in conjunction with 27

other informat ion, to back

- calculate the fraction of illnesses attributed to various 28

scenarios. This approach is infrequently used in microbial exposure assessment because 29

epidemiologic al surveillance data is prone to data gaps that may not be well characterized 30

(under reporting of illnesses is significant and varies). Nevertheless, national 31

surveillance systems combined with thorough investigations of outbreaks may generate 32

sufficiently valid conclusions about sources and causes of those illnesses. If such data 33

are a vailable, it is feasible to determine the number of exposures from a target source that 34

cause human illnesses. Furthermore, the estimated number of illnesses that result from 35

the target source can be divided by the estimated number of total exposures to d etermine 36

the probability of illness per exposure. 37

38

Ultimately, the number of illnesses from a microorganism that are attributed to a 39

source represents the total number of illnesses that could be avoided by eliminating that 40

source. If complete eliminatio n of the source is not feasible, then further analysis might 41

suggest what fraction of a baseline number of cases could be avoided by a proposed risk 42

management option that improves the effectiveness of some particular process. 43

44

For illustrative purposes, a ssume 1,000 cases of illness caused by microorganism 45

B are detected by public health surveillance each year (Figure 5

. 2). Analysis of the 46Microbia l Risk Assessment Guideline Page 107

Draft

– Do not cite, circulate, or quote surveillance system suggests that only one of every 50 cases that occur is detected via 1

this system. Therefore, the true number of cases is estimated to be 50,000 per year. 2

Epidemiologic evidence suggests that 20 percent of cases are caused by Source 1 (e.g., 3

ground beef). Research evidence suggests that 50 percent of source 1 exposures are 4

directly attributable to pr ocess 1 (e.g., under

- cooking). Based on these values, you 5

estimate the number of illnesses attributed to Source 1 is 50,000 0.20=10,000 . You 6

estimate the number of those illnesses attributed to process 1 is 10,000 0.50=5,000

. 7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

Figure 5

. 2 Schematic illustrating direction of inference when using an attribution 23

approach to exposure assessment

. 24

25

Process M odeling 26

27

For many exposure assessments, planning and scoping will suggest that 28

attribution modeling is inadequate. A dearth of reasonable ep idemiologic surveillance 29

evidence sometimes precludes attribution modeling, as well. In these cases, a process 30

modeling approach can be used. 31

32

Generally, process modeling involves the characterization of processes that occur 33

from the initial source of th e microbe to the point of exposure. The influence of 34

processes associated with growth or attenuation of microbial populations is one of the 35

purposes of the predictive microbiology field. Research of microbial kinetics and 36

lethality provides important ins ight to process modeling. 37

38

Simulation of the effects of processes on microbial occurrence is commonly 39

accomplished using Monte Carlo sampling techniques (see S ection 5.1.11). 40

Nevertheless, simple probabilistic models may not require Monte Carlo sampling and 41

other dynamic modeling methods, such as those based on differential equations, may be 42

solvable using other math ematical techniques (e.g., Cox, 2006). 43

44

Regardless of the technique, process modeling is concerned with predicting 45

exposures given the init ial microbial occurrence. Its direction of inference is opposite 46Page 108 Microbia l Risk Assessment Guideline

that of attribution modeling (Figure 5

. 2) and calculations tend to follow an input

- output 1

flow from initial microbial levels through each process in the model. 2

3

Process modeling begins with some prediction of microbe occurrence prior to 4

exposure (Figure 5.3) . Processes that occur subsequent to this beginning point influence 5

the microbial population to grow, maintain, or decline. These processes have been 6

referred to as transformations and t he mathematic effect of these transformations is 7

sometimes modeled using transformation ratios (Haas et al., 1999). Such ratios simply 8

describe the level of microorganisms after the process to the level before the process. 9

You can define 10

11

(Eq 5.2) 12

13

an d stipulate that this value is reasonably estimated from predictive microbiology. It 14

might also be invariant to the initial microbe level so that the ratio is constant across all 15

amounts of the microbe. If you are given an amount of a microbe before the process 16

( x j 1 ) , you can calculate the amount of microbe after the process as x j = j x

j 1 , where 17

the j subscript could refer to a time step somewhere between the beginning and end of the 18

model when process j is applied. For example, time step j may be a decontamination step 19

applied to water, food or something else in which the microorganism occurs. If the 20

exposure assessment predicts the amount before the decontamination process, then this 21

equation predicts the amount after that step. Nevertheless, j is an abstraction of the 22

complicated interactions that likely exist between microorganisms and their environment. 23

Sometimes those complex interactions need to be explicitly modeled using more 24

complicated math and/or simulation. Note that this approach do es not incorporate cases 25

where toxin generation during processing, before exposure is an issue. 26

27

A modular approach to exposure assessment process models has been suggested 28

for food safety applications (Nauta, 2002). One advantage of subdividing a model i s that 29

complicated components in one module can be summarized into a simpler format before 30

propagating the output of that module to the next module. For example, a module may 31

predict microbial growth as a function of storage times and temperatures and oth er 32

factors. The resulting prediction may be a continuous distribution describing the 33

exponential growth of the microbial populations that are input to this module. 34

Nevertheless, it may only be necessary to consider a small number of discrete levels of 35

mi croorganisms in the next module (e.g., by log 10 units), so this module‘s continuous 36

results can be summarized before calculations in the next module are completed. 37

38

39

40

41

42

43

44Microbia l Risk Assessment Guideline Page 109

Draft

– Do not cite, circulate, or quote 1

2

3

4

5

Figure 5

. 3 Schematic illustrating direction of inference when using a process 6

modeling approach 7

8

5.2.5 How is Scenario Analysis Used in Exposure Assessment ? 9

10

Scenario analysis begins the process of identifying those scenarios that constitute 11

a risk as we ll as those that are not a risk. It simplifies the next stage of exposure model 12

development; quantifying the likelihood of those scenarios that end in an exposure dose. 13

14

In developing scenarios during the conceptual development phase, you also start 15

the process of defining the mathematical relationships between steps. Are these additive 16

or multiplicative? Are there correlations between different inputs? What other factors 17

influence the relationships among inputs? 18

19

Compared to scenario development, d etermining likelihood and dose is usually a 20

more mathematical exercise. The relationships between model inputs need to be 21

mathematically described; often statistical methods are employed to quantify these 22

relationships. Also, the process of converting co nceptual relationships into explicit 23

mathematical relationships commonly involves additional assumptions beyond those 24

represented in the conceptual model. 25

26

Before data availability or analysis is considered, the mathematical development 27

needed to determine likelihood and dose should be determined. Once a tentative 28

mathematical model is completed, its structure can be communicated with outside 29

reviewers and risk managers. Typically, the mathematics will provoke discussion about 30

the data needs of the model. A clearly defined mathematical model provides an 31

opportunity for specialists to review and comment on the course of the project. If 32Page 110 Microbia l Risk Assessment Guideline

scrutiny of the mathematics determines the need for change, then you can make changes 1

before a large investment in data a cquisition and analysis is completed. Frequent 2

outreach and feedback is one manifestation of effective exposure assessment. 3

4

An Illustrative E xample 5

6

In a very simple illustrative example, you want to predict the exposure risk from 7

servings containing 10 units of microbe B (Figure 5

. 4). Two scenarios are considered: 8

one where the serving is cooked such that all the microorganisms in the serving are 9

destroyed and another where the serving is not cooked and all the microorganisms 10

survive. From available da ta or expert opinion, you determine that the likelihood of 11

cooking such servings is f , where 0≤ f ≤1 . Therefore, the likelihood ( l 1 ) of the cooking 12

scenario ( s 1 ) is f which is inseparable from an exposure dose ( x 1 ) of zero units of 13

microorganism B. 14

15

16

17

Figure 5

. 4 A simple illustrative example of two exposure scenarios resulting from 18

an initial amount of microorganism B in a serving of food. In this case f is the 19

probability that a serving will be cooked such that all of microbe B is destroyed. 20

Convers ely, 1

-

f is the probability that a serving will not be cooked and the total 21

amount of microorganism B survives to expose a consumer. 22

23

In a slightly more complicated illustrative example, you consider three average 24

concentrations of microorganism B per serv ing, three levels of cooking effectiveness, and 25

three amounts of food or water consumed per serving (Figure 5

. 5). This example 26

generates 27 different scenarios for the exposure assessment. 27

28

Besides the complication of more scenarios, this example also u ses a 29

concentration measure for microorganism levels instead of a fixed number of 30

microorganisms in a serving. Concentrations are used in many food and water microbial 31

exposure assessments. It is sometimes recommended that modeling microbial 32

concentratio n be avoided and physical counts of microorganisms should instead be 33

explicitly tracked (ECSSC, 2003; Nauta, 2005). One justification for such an approach is 34

that it avoids mass balance mistakes that can occur when processes necessarily change 35

the microbi al numbers. For example, a partitioning of some bulk quantity into subunits 36Microbia l Risk Assessment Guideline Page 111

Draft

– Do not cite, circulate, or quote necessarily must account for all the organisms that existed in the bulk quantity. Yet, an 1

average concentration per subunit might result in an incorrect assessment of the exposur e 2

dose per subunit. Nevertheless, for problems without partitioning or mixing processes, 3

using concentration may be sufficient. 4

5

You may assume this example pertains to some bulk product in which three 6

average concentrations (e.g., microorganisms per mi lliliter or per gram) are possible. 7

You define each concentration as 1 , 2 , and 3 and refer to the likelihood of each of 8

these as f

( 1 ), f ( 2 ), and f

( 3 ) . A similar approach is used for cooking effectiveness levels 9

( i and g ( i ) ) and consumption amount s ( i and h ( i ) ) to define their values and 10

likelihoods 33

. 11

12

13

14

15

Figure 5

. 5 An illustrative example of a slightly more complicated exposure 16

assessment 17

18

The dose and likelihood for each of the 27 scenarios generated for this exposure 19

assessment can be calcul ated directly from the information given. For example, the 20

average dose for scenario 1 ( x 1 ) is the product of concentration ( 1 ), cooking 21

effectiveness ( 1 ) and serving size consumed ( 1 ). Similarly, the likelihood of scenario 1 22

( l 1 ) is the joint probabi lity of each of these events occurring; if you know these events are 23

independent, then l 1 ( x 1 , 1 , 1 , 1 ) = f ( 1 ) g( 1 ) h( 1 ) . 24

25

The multiplications for this simple model are common for many process models. 26

Exposure dose is often the result of mult iplicative, input

- output, relationships; in this case 27

the average dose for a scenario is . The unit of x i is microorganisms per 28

33 Note that branch likelihoods must sum to one (i.e., ) so if we know two of the likelihoods

then the third is also known (e.g., g ( 3 ) = 1 g ( 1 ) g ( 2 )

.Page 112 Microbia l Risk Assessment Guideline

serving; i is microorganisms per volume or mass; I is unitless ratios; and i is volume 1

or mass per serving. Predictive micr obiology sometimes prefers to represent microbe 2

quantities in log 10 units. A logarithmic treatment will convert the multiplicative 3

calculations of the model to addition or subtraction (e.g., 4

) while the joint likelihood remains the product of the 5

likelih oods of each variable in the model. 6

7

Although the results from this example are trivial, it is illustrative to interpret 8

them. These results explain which scenarios generate the highest doses and the 9

likelihoods that those doses occur. By summing the l ikelihoods of scenarios in which 10

zero organisms occur, the prevalence of non

- zero exposures can be determined (i.e., 11

). 12

13

The relative importance of different concentrations, cooking effectiveness levels 14

and amounts consumed might also be assessed by cal culating conditional expected dose 15

values. For example, the conditional expected dose value for one concentration 16

( ) is the average dose calculated when only that concentration is considered 17

but all the values for cooking effectiveness and amounts consum ed are still possible. 18

19

(Eq 5.3) 20

21

The magnitudes of the conditional expected values suggest the influence of these 22

inputs on the average exposure dose (Table 5

. 3). For example, the largest change in 23

average dose occurs across the possible values for initial concentration, but the smallest 24

average dose occurs if cooking is completely effective. 25

26

27

28Microbia l Risk Assessment Guideline Page 113

Draft

– Do not cite, circulate, or quote Table 5

. 3 Results of a simple exposure assessment 1

2

3

As the previous example illustrates, explicit tree

- diagram schematics of exposure 4

scenarios are daunt ing when the inputs can assume multiple values and the number of 5

processes is increased. A full graphic depiction of all 27 scenarios for the previous 6

example would not fit on a single page. 7

8

For more complicated exposure models, the inputs are simply t reated as random 9

variables that are mathematically combined. In the simple example, you can represent all 10

three possible values for initial concentration as where the tilde symbol signifies that 11

concentration is a random variable. You can similarly defin e and

. 12

13

Using symbols for the random variables in the model, exposure can be 14

mathematically written as where the dose delivered to humans is also a 15

random variable by virtue of the fact that it is a function of random variables. Statistical 16

moments of (e.g., its mean and variance) might be predictable, but when the likelihood 17

distributions for

, and involve more than a trivial number of values, other 18

techniques (e.g., Monte Carlo simulation) are often used to determine . Nevertheless, 19

the tech niques essentially mimic the procedure followed for the simple example; the 20Page 114 Microbia l Risk Assessment Guideline

possible values for

, , and are multiplied together and their joint likelihood is 1

determined. 2

3

Once an exposure model becomes more complex, the identities of individual 4

scenarios a re difficult to determine. It is common for exposure assessments to focus on 5

predicting the likelihood of doses without explicitly identifying scenarios. In these 6

situations, sensitivity analysis is used to sort out the relative influence of model inputs on 7

the exposure distribution. Nevertheless, it is sometimes crucial to identify the higher risk 8

scenarios; thinking about the model as a scenario tree is one useful technique for 9

elucidating those scenarios. 10

11

Random variables can be discrete or continuou s. Exposure assessments often use 12

a mixture of both. Although it is intuitively appealing to consider microbial counts as 13

discretely distributed random variables, it is not always essential that they be treated as 14

such. Naturally continuous distribution s, like weight measures or measures of 15

effectiveness, may also be treated as discrete random variables in some models to 16

simplify their calculations without any loss of information. Like most decisions in 17

exposure assessment, the choice of distribution is informed by planning and scoping. 18

Nevertheless, such decisions should be made with an understanding of the biologic 19

plausibility of the choice. 20

21

Determining the likelihood of doses of microorganisms is the fundamental 22

objective of most exposure assessmen ts. The key to effective exposure assessment, 23

therefore, is to explain the mathematical relationships among the random variables that 24

contribute to exposure. Once you explain and justify the mathematical model, the 25

process of determining the exposure dis tribution is relatively straight

- forward. 26

Nevertheless, much of the work of conducting an exposure assessment involves 27

collection and analysis of available data for the different random variables in the model, 28

as well as explicit representation of the unc ertainty about inputs to the model or the 29

model itself. 30

31

5.2.6 What is the Role of Predictive Microbiology in Exposure Assessment? 32

33

The field of predictive micro biology is important to m any microbial exposure 34

assessments. This field is concerned with quantifying the dynamics of microbial 35

populations and these dynamics often depend on environmental and other biologic 36

factors. Useful discussions on the mathematics and statistics of predictive microbiology 37

are available (Ross and McMeekin, 1994, 2003; Haas et al., 1999; ECSCC, 2004 ; Vose, 38

2008 ). These references also cite seminal research in this discipline. Within the field of 39

food safety, the Center of Excellenc e in Microbiological Modeling provides research and 40

products related to pathogen modeling

. 34 41

42

Functional relationships that describe microbial dynamics are typically of an 43

input

- output form. For example, an exponential growth model is; 44

34 http://ars.usda.gov/Services/docs.htm?docid=8392Microbia l Risk Assessment Guideline Page 115

Draft

– Do not cite, circulate, or quote 1

2

where and are th e number of microorganisms at times t and zero, respectively, and k 3

is an exponential growth rate constant. Rearrangement of this relationship illustrates 4

derivation of a transformation ratio for growth; 5

6

(Eq 5.4) 7

8

If the constant, k , in the above equa tions is a negative value, then the same relationship 9

can serve to predict attenuation of microorganisms in time unit t. 10

11

The exponential growth rate is only constant for particular environmental 12

conditions. At a minimum, most exposure assessments will co nsider environmental 13

conditions to be variable between scenarios. In such cases, k is some function of 14

temperature, pH and/or other conditions (i.e., k = f ( environmental conditions ) ). The 15

reader should refer to the Center of Excellence in Microbiological Modeling or the 16

FoodRisk.org 35 websites for research regarding how various environmental factors

– and 17

microbial strain differences

– influence microbial growth and/or attenuation behavior. 18

Specific guidance for statistical fitting of experimental microbi al growth or attenuation 19

data can be found in these references. 20

21

Growth or attenuation functions can be deterministic (i.e., one set of parameters 22

predicts one amount of change) or stochastic (i.e., one set of parameters predicts a 23

probability distrib ution for amount of change). In the context of an exposure assessment, 24

however, the predictions from either a deterministic or stochastic function will be 25

stochastic because the environmental parameters upon which growth or attenuation 26

depend are variable s . This source of variability relates to human behaviors such as 27

storage times and temperatures that vary across individuals. 28

29

Clearly, human behavior can be highly variable; and behaviors such as exposing 30

raw foods to high temperatures for extended per iods of time can dramatically affect the 31

dose of pathogens ultimately consumed. Although data on microbial growth or 32

attenuation behavior can be generated readily in experimental laboratories, data on 33

human food handling behaviors must be collected via we ll

- designed human population 34

surveys. Such data are available for some commodities, such as ready

- to

- eat foods (Kosa 35

et al., 2007; Pouillot et al., 2010), but not necessarily for all food commodities. Although 36

data regarding refrigeration temperatures ma y be applicable to most perishable foods, 37

storage time within the refrigerator may depend on the particular food; this phenomenon 38

can only be captured via food

- specific surveys. In addition, times and temperatures that 39

foods experience during transport fr om retail to homes, during food preparation and prior 40

to (or following partial) consumption are sometimes needed. Actual human behavior 41

35 http://www.foodrisk.org/resource_types/tools/predictive_micro.cfmPage 116 Microbia l Risk Assessment Guideline

data that captures variability in cooking processes is also sometimes important for 1

estimating the microbial attenuatio n achieved prior to consumption. 2

3

You can apply m ore complex growth and attenuation models in exposure 4

modeling. For example, the Gompertz equation

– or modifications thereof

– includes 5

specific parameters for lag time and asymptotic maximum density ( Haas et al., 1999). 6

Lag time is a characteristic of many growth curves; measured from time zero, it is the 7

elapsed period before exponential growth begins. The maximum density that a microbe 8

can attain before competition for nutrients among the microorga nisms halts growth is 9

another modeling characteristic studied by predictive microbiologists. 10

11

Predictive microbiology provides insight and data concerning the behavior of 12

microorganisms across different environmental conditions. Nevertheless, such insight 13

and data needs to be translated and extrapolated from experimental studies to natural 14

conditions when applied to exposure assessment. Adjustment of results from controlled 15

experimental settings to highly variable (and uncertain) natural conditions can be 16

difficult. Therefore, care should be taken when applying predictive microbiology to 17

exposure assessment. 18

19

Common difficulties for direct application of predictive microbiology to exposure 20

assessment are: accounting for variable temperatures across time a nd accounting for the 21

competitive effects of other microorganisms on the growth characteristics of a target 22

microbe. Varying temperatures suggest variable transitions between growth, 23

maintenance and attenuation of microbial populations. Depending on whet her growth or 24

attenuation is a process with memory or is memory

- less, the modeling techniques needed 25

for microbial dynamics will be different (Vose, 2008) . The existence of other 26

microorganisms in media can influence the growth rate or maximum density a t arget 27

microbe can achieve , which is termed the Jameson effect (Ross and McMeekin, 2003). 28

29

ILSI (2010) looked at mechanisms that have an impact on physical distributions, 30

characteristics of frequency distributions employed to model microbial distributions, and 31

the impact of both physical and frequency distributions on illness risk and food safety 32

management criteria. ILSI outlined six mechanisms that can impact the microbial 33

distribution in a foodstuff: contamination, growth, death, joining, mixing, and 34

fr actionation (ILSI, 2010). 35

36

Although not necessarily a part of predictive microbiology, the processes of 37

microbial transfer and cross

- contamination are also not well researched. There is a need 38

for further development of modeling approaches for these poten tially important 39

processes. Some default techniques have been suggested for use in food safety 40

applications (ECSCC, 2003). 41

42

43Microbia l Risk Assessment Guideline Page 117

Draft

– Do not cite, circulate, or quote 5.2.7 How Can I Address Secondary Transmission of Disease in the 1

Population? 2

3

The approach described heretofore assumes that exposures result directly from the 4

media of interest (e.g., food or drinking water) and the potential for person

- to

- person 5

transmission of disease is not taken in to account. Such an approach generally assumes 6

that multiple or recurring exposures constitute independent events with identical 7

distributions of contamination (Regli et al., 1991). Furthermore, secondary transmission 8

and immunity are most often assumed to be negligible in this approach. Nevertheless, 9

such assumptions may not be valid. 10

11

To more completely assess all possible exposures, it may be necessary to consider 12

possible secondary transmissions that result from a primary infection. Such an approach 13

commonly requires consideration of a disease transmission model. A variety of models 14

have been formulated, mathematically analyzed, and applied to infectious diseases 15

(He thcote, 2000) . Mathematical models of disease transmission have become important 16

tools that have led to understanding the transmission characteristics of infectious diseases 17

in communities and better approaches to decreasing the transmission of these dis eases 18

(Hethcote, 2000; King et al., 2008; Riley et al., 2003) . Modeling infectious disease 19

processes such as person

- to

- person transmission of infection and immunity requires 20

dynamic methods where the number of ind ividuals that are assumed to be susceptible to 21

infection is time

- varying and risk is manifest at the population level (Anderson and May, 22

1991; Hethcote, 1976, 2000)

. 23

24

Epidemiologic al disease transmission models str atify a population of potentially 25

exposed humans into different states according to disease status: 26

27

a) S usceptible 28

29

b) D iseased (infectious and symptomatic) 30

31

c) I mmune (partial or complete) 32

33

d) C arrier (infectious but asymptomatic) 34

35

Only a portion of the population i s in the susceptible state at any point in time, 36

and only those individuals in a susceptible state can become infected through exposure to 37

pathogens. Members of a population may move between model states and model 38

parameters predict the numbers of people that are in each of the epidemiological states at 39

any given point in time. Factors affecting the population dynamics include the level and 40

frequency of exposure, the ability of individuals in infectious states to infect susceptible 41

individuals, and the te mporal processes of the disease (e.g., incubation period, duration of 42

disease, duration of protective immunity ). The rate parameters may be determined 43

through literature review or through site

- specific data. 44

45Page 118 Microbia l Risk Assessment Guideline

Disease transmission models may also be used to determine the primary exposure. 1

Such models may be necessary to predict the level and frequency of contaminated media 2

when little or no empiric evidence is available. For example, assessing exposures that 3

result from the inadvertent slaughter of a Hig hly Pathogenic Avian Influenza

- infected 4

U.S. poultry flock requires modeling the epidemic spread o f the virus within that flock 5

( USDA , 2008). This approach is needed because there is no evidence available 6

concerning the occurrence of this pathogen among U .S. poultry flocks. 7

8

Secondary transmission modeling for airborne microorganisms such as anthrax 9

and s evere acute respiratory syndrome (SARS) virus may be discussed in more detail in a 10

future volume of this Guideline (Riley et al., 2003; Bartrand et al., 2008)

. 11

12

5.2.8 What Data Can I Use in an Exposure Assessment? 13

14

Ideally, the data needed for an exposure assessment are determined by its specific 15

conceptual and mathematic models. If the needs are clearly determined before effort is 16

expended in collecting and analyzing data, a fuller and more efficient treatment of 17

relevant data can be accomplished. 18

19

Exposure assessment s data usually stem from either population

- or experimental

20

based surveys or studies. These data are preferably from published or reviewed research 21

and are fully relevant and representative of what is needed in the exposure assessment

. 22

However, this is not always the case. An extensive discussion of data type s and sou rces 23

is available (WHO/ FAO, 2008). 24

25

The broad categories of data needs for exposure assessment are: microbial

, 26

processes, and exposed humans. Within each of these categories, however, is an array of 27

data types that may be needed for specific analyses. 28

29

Data on M icroorganisms 30

31

Data about the occurrence and amount of microorganisms within the medium of 32

intere st (e.g., water, food, air ) is important for process modeling. It is desirable to have 33

occurrence data for multiple points between the beginning of the model and the point of 34

exposure. 35

36

Prevalence data provide presence/absence data for the occurrence of a 37

microorganism in a medium. Such data support estimation of the proportion of some 38

population in which the microbe occurs (i.e., ) 39

during some c ross

- section of time. Observational studies that solely report prevalence 40

are rarely directly applicable to exposure assessments. Instead

, the apparent prevalence 41

must be adjusted for the probability that units with the microbe would be detected if 42

actua lly present (i.e., sensitivity) in order to estimate the true prevalence. Apparent 43

prevalence is also influenced by the probability that units without the microbe might be 44Microbia l Risk Assessment Guideline Page 119

Draft

– Do not cite, circulate, or quote incorrectly detected (i.e., 1

– specificity) , for example, if there were cross

- reac tivity in an 1

immunological assay

. 2

3

Counts of microorganisms in sampling studies are desirable for exposure 4

assessments. Such data may arise from microbiologic techniques such as direct plating, 5

observation or most probable number assays (Haas et al., 19 99). These data provide an 6

empirical distribution of the number of samples that contained each count of microbe 7

observed. Nevertheless, it is important to also know the sensitivity and specificity of the 8

methods used in count assays to accurately interpr et the data. 9

10

A well

- designed exposure assessment fully characterizes the microbe (or 11

microorganisms) on which it is focused. Oftentimes, occurrence data will not be specific 12

for the target microbe and additional data will be needed to interpret the relev ance of the 13

occurrence data. For example, count data for all Salmonella serotypes on broiler 14

carcasses would need to be adjusted if the focus of the exposure assessment was 15

Salmonella enteric T yphimurium . If you knew that the count data were adequately 16

d escribed by a Poisson distribution and you also estimated that 20% of all Salmonella 17

were Salmonella enteric Typhimurium , then you might simply model the counts of 18

Salmonella enteric Typhimurium as distributed according to a Poisson ( ) 19

distribution. Yet, other data may suggest this simple approach does not adequately 20

account for the clustered occurrence of specific Salmonella serotypes on broiler 21

carcasses. 22

23

Process D ata 24

25

General processes common to many exposure assessments include growth and 26

attenuatio n of the target microbe(s), as well as mixing and partitioning of the medium in 27

which the microbe occurs (Nauta, 2002). The processes modeled in an exposure 28

assessment should be informed by the evidence used to construct the conceptual model. 29

30

Predicted changes in microbial amounts resulting from growth or attenuation 31

processes may be available from predictive microbiology research. Nevertheless, these 32

predictions are often functionally dependent on environmental factors. Therefore, you 33

need data to ch aracterize the variability in parameters such as temperature and time in 34

order to employ predictive microbiology in an exposure assessment. 35

36

Partitioning of water, food or air into smaller subunits prior to exposure is a 37

common problem in exposure assess ment that requires industry or ecologic data to solve. 38

Mass balance considerations may be required to account for recycling or cross

39

contamination of microorganisms in some scenarios. Some of these data may come from 40

industry

- or government

- sponsored sur veillance systems; but sometimes expert 41

experience will be the only information available. 42

43

44Page 120 Microbia l Risk Assessment Guideline

Human Characteristics a nd Behavioral Data 1

2

Demographic and behavior data concerning exposed humans will be specific to 3

the subpopulations and media considered in the exposure assessment. Much of the data 4

used to characterize subpopulations will come from routine government surveys. These 5

surveys provide demographic data by geographic region, age, sex and other factors. The 6

estimated proportion of the population that represents a susceptible population may be 7

available from epidemiologic research. Extrapolations from non

- representative data may 8

require substantial modeling and expert judgment to accomplish. 9

10

Data on human behaviors that influence the exposure assessment will be needed. 11

Some behaviors, like time and temperature of storage or cooking, are highly variable 12

among the human population. Some of these behaviors have been the subjects of on

13

going research projects

. 36 These data inform the growth and i nactivation processes via 14

their predictive functional relationship with microbial counts. 15

16

Specific data on some human behaviors that increase the likelihood of exposure to 17

a particular microbe (e.g., preference for raw meats or seafood, occupational expos ure to 18

microbe rich environments, cohabitation with infected individuals) are sometimes 19

difficult to locate. Nevertheless, some frequency and contact rates have been 20

summarized for water and air media (Haas et al., 1999). Similarly, government surveys 21

ca n provide data on variability in consumption, inhalation or contact amounts across 22

individuals and groups of individuals

. 37 23

24

Some well known and frequently used sources of human consumption data 25

include CDC‘s National Health and Nutrition Examination Surv ey (NHANES) 38 and 26

USDA ‘s Continuing Survey of Food Intakes by Individuals (CSFII) , which as of 2002 27

ha ve been integrated and maintain the name NHANES (Dwyer, 2003). The FoodNet 28

P opulation Survey Atlas of Exposures is also a useful resource. 39 29

30

5.2.9 How do I Use Data in an Exposure Assessment? 31

32

Data provide evidence about the inputs to the exposure assessment, but data also 33

influence the magnitude of uncertainty surrounding its results. Weak or absent data are 34

usually associated with large uncertainties while data that are substantial, relevant and 35

representative contribute little uncertainty to an exposure assessment. 36

37

Population

- based, observational data are commonly used to estimate th e 38

parameters for random variables in exposure assessments. Statistically robust approaches 39

for selecting appropriate probability distributions, estimating the parameters of those 40

distributions and comparing alternative distributions are explained elsewher e (Vose, 2008 41

and Haas et al., 1999). Often the process of fitting data to distributions is complicated 42

36 www.cfsan.fda.gov/~lrd/ab

- foodb.html

37 www.ars.usda.gov/ma in

38 http://www.cdc.gov/nchs/nhanes.htm

39 http://www.cdc.gov/foodnet/surveys/FoodNetExposureAtlas0607_508.pdfMicrobia l Risk Assessment Guideline Page 121

Draft

– Do not cite, circulate, or quote because the data were generated by imperfect detection systems. Adjustments for 1

imperfect detection sensitivity and specificity are discussed in those same references. 2

3

Although there are pros and cons to strict application of either classic statistical 4

(―frequentist‖) or Bayesian estimation methods, it is often the case that the results of the 5

two approaches are very similar. Results will tend to di ffer when the available 6

dose

‐ response data are very limited and/or when there is substantial information other 7

than the numerical dose

‐ response data that leads to a very informative (i.e., precise) prior. 8

9

Procedures for statistical fitting of data to dist ributions include appropriate 10

methods for determining the uncertainty in the estimated distribution parameter(s). This 11

uncertainty is propagated through the exposure assessment and combined with other 12

sources of uncertainty to quantify the total uncertain ty about the resulting exposure 13

distribution. 14

15

Data of questionable relevance to the specific exposure assessment require special 16

consideration. Similarly, data that are not entirely representative of the populations 17

modeled should be carefully used. It can be argued that data pertaining to one microbe 18

are also relevant to another. Such  ̳surrogate‘ relationships should only be exploited 19

when there is an absence of data that is directly relevant. Establishing the credibility of a 20

surrogate for the target microbe may require a high standard of proof. It is usually 21

preferred to use directly relevant data and honestly represent its uncertainty than to mix 22

highly relevant and surrogate data in an attempt to reduce uncertainty. 23

24

Highly representative data are generated from random sampling of all relevant 25

subpopulations. For example, an exposure assessment pertaining to the United States 26

would preferably use human behavior data generated from a representative sample of 27

U.S. persons. Nevertheless, representat ive data may not be available for some model 28

inputs. Instead, data from specific regions or other countries may be available. Based on 29

comparison of other factors it may be concluded that one or more of these other data 30

sources could be a reasonable subs titute. In such cases, you can make an effort to 31

determine the best substitute and only use its data in the exposure assessment. It is 32

usually inappropriate to mix multiple sources of less representative data because the 33

resulting estimates often imply m ore confidence than is legitimate to claim. 34

35

5.3 How do I Analyze a Model’s Results? 36

37

The general purpose of an exposure assessment is to translate the technical inputs 38

of a model into a descripti on of the likelihood of exposure doses in some defined part of 39

the human population. Quantitative assessments estimate numeric values while 40

qualitative assessments may use ordinal metrics (e.g., high, medium, and low) to signify 41

the magnitudes of exposure s. 42

43

Quantitative e xposure assessment models usually comprise random variables that 44

consequently generate variability in exposures. Therefore, a common output of the 45

exposure assessment is the frequency distribution of possible doses that come in contact 46Page 122 Microbia l Risk Assessment Guideline

with the human population of interest. This distribution is an estimate of the actual 1

variability in exposures that occurs in nature. If the model is calculated based on input 2

values thought to accurately reflect current conditions, this variability ref lects what is 3

occurring at present. Exposure assessments often refer to such predictions as the baseline 4

exposure. If the model is calculated based on proposed risk management changes, then 5

the variability reflects a prediction about the future. 6

7

It sh ould be noted that the exposure distribution calculated from a model is also 8

constrained by some unit of time. This time component must be explicit to correctly 9

interpret and extrapolate the variability predicted by the model. 10

11

Analysis of the exposure distribution includes determining the sensitivity of this 12

distribution to changes in the random variables (or other model inputs) used to predict it. 13

Sensitivity analysis determines which model inputs are the primary drivers or predictors 14

of substantial d oses with relatively high likelihoods of occurring. Conclusions about the 15

important inputs directly inform risk managers by suggesting what changes in the system 16

will cause the greatest reduction in exposures. 17

18

If all model inputs were perfectly known , then the output of an exposure 19

assessment might arguably consist of a single exposure distribution. Yet uncertainty 20

potentially pervades all aspects of an exposure model and the resulting uncertainty about 21

its predictions can be incorporated into any an alysis. Uncertainty analysis is concerned 22

with determining the influence of the various sources of uncertainty on the predictions of 23

the exposure assessment. 24

25

5.3.1 How do I Report Exposure in an Exposure Assessment? 26

27

The natural output of an exposure assessment is an exposure distribution; this 28

distribution provides likelihood or frequency values for the range of possible doses that 29

constitute exposures. You should clearly id entif y the applicable time period and the 30

exposed human population to which the exposure distribution applies. The most 31

common format for reporting exposures is tables or graphs. 32

33

An exposure distribution may reflect the possible doses an individual could 34

experience in, for example, one year. The objective of the risk assessment should 35

determine the type of exposure distribution reported. It could reflect those doses relevant 36

to a highly sensitive population , life stage, or to the entire population. 37

38

An exposure assessment will convey the variability of doses for the relevant 39

population per unit time, but it may also include consideration of the variability of the 40

entire distribution across time. A dynamic exposure assessment that predicts trends in 41

expo sure across time may report the trends based on the statistical expected values of the 42

individual exposure distributions or it may report the actual distributions for each time 43

period considered. 44

45Microbia l Risk Assessment Guideline Page 123

Draft

– Do not cite, circulate, or quote It is essential that you communicate the magnitude of uncer tainty about the true 1

exposure distribution. A number of techniques may be used to convey the uncertainty, 2

but no method is universally applicable and all methods can be computationally intensive 3

(Lammerding and Fazil , 2000; Cullen, 1999) . Second

- order m odeling

– in which 4

variability is derived for one combination of uncertain inputs and the process is repeated 5

until a full range of plausible combinations is achieved

– is a common technique to 6

accomplish a clear separation of variabil ity from uncertainty (Vose, 2008 ). Nevertheless, 7

the complexity of this technique may preclude its widespread application. 8

9

Multiple exposures to various doses for the same individual can complicate 10

exposure assessments. It is not uncommon for exposure assessments to assum e that 11

exposure to non

- zero doses is an infrequent phenomenon within a population. Therefore, 12

it is typically assumed that there is a one

- to

- one correspondence between exposures and 13

individuals, or at least the same individual is unlikely to be exposed tw ice within a short 14

period of time. Nevertheless, in some cases this assumption is not robust, especially 15

when considering exposures that may be clustered in space and time. Microbial dose

16

response models typically describe the likelihood of an adverse ou tcome as a function of 17

a single exposure to some dose. If persons are expected to face multiple exposures, then 18

the process of combining the exposure information with a typical dose

- response function 19

is be different from a standard one

- dose one

- person app roach (Haas et al., 1999). 20

21

5.3.2 How do I Determine a Change in Exposure and Subsequent Risk? 22

23

The purpose of many MRA s is to determine how risk management decisions 24

mi ght change the risk of an adverse human health outcome. From the perspective of an 25

exposure assessment, this usually requires calculating the model with and without the 26

proposed change. For example, risk managers may want to evaluate the effect of some 27

n ew mitigation process on the frequency or level of exposure doses. A baseline exposure 28

prediction is compared to an exposure prediction based on the inclusion of the new 29

mitigation process. 30

31

In large exposure models, measuring change in exposure is comp licated by the 32

role of uncertainty in the model‘s predictions. The baseline model predicts an exposure 33

distribution with its attendant uncertainty. The mitigation model will predict a different 34

exposure distribution with its own attendant uncertainty. B ut, the uncertainties between 35

the two predictions are not independent of each other. It should be clear that the same 36

things that contribute uncertainty to the baseline predictions usually apply to the 37

mitigation predictions. Therefore, measuring the cha nge in exposure between the two 38

predictions is not trivial. Typically, the change you are interested in quantifying is in the 39

number of adverse human health outcomes. That change cannot be calculated until risk 40

characterization takes place and the proble m of dependent uncertainties is compounded 41

by the inclusion of dose

- response uncertainty. 42

43

A direct method for accurately measuring changes in exposures or adverse human 44

health outcomes is to model the baseline and mitigation predictions simultaneously usi ng 45Page 124 Microbia l Risk Assessment Guideline

some parallel modeling structure. This method is computationally daunting, however, 1

and is not commonly followed. 2

3

Another approach can provide boundaries for the magnitude of change in 4

numbers of adverse human health outcomes. In this approach, two uncertainty 5

distributions about numbers of adverse human health outcomes, generated by separately 6

calculating the baseline and mitigation predictions, are subtracted from each other 7

assuming perfect positive correlation and assuming perfect independence. Generic 8

equations for each approach are: 9

10

(Eq 5.5) 11

12

(Eq 5.6) 13

14

15

(Eq 5.7) 16

17

These equations provide the expected value and variance of the difference using 18

the two approaches. It may be appropriate to assume some parametric distribution for 19

change in a dverse human health outcomes; in that case these moments can be used to 20

determine that distribution‘s parameters. The results assuming independence and perfect 21

correlation provide boundaries for the more accurate predictions that could be generated 22

using parallel calculation. In some cases, the bounds may not be sufficiently different to 23

cause concern but in other cases this analysis may suggest the need to invest in the 24

development of a parallel model structure. 25

26

5.3.3 What is Sensitivity Analysis? 27

28

Sensitivity analysis examines the relative influence and importance of a model‘s 29

inputs on its output (see Section 6.7) . A well

- designed exposure assessment model 30

should comprise inputs that influence ex posures, so the important idea of sensitivity 31

analysis is measuring the  ̳relative‘ influence. Sensitivity analysis may be completed as 32

part of an exposure assessment or it may be done as part of risk characterization. 33

34

There is no universal standard for conducting sensitivity analysis (Frey et al., 35

2003). In fact, multiple approaches may be legitimately used because each approach may 36

examine a different type of influence. The typical sensitivity analysis examines the 37

magnitude of change in exposures f or some change in inputs (i.e., ). One 38

challenge is determining how a change in exposures should be measured. In some cases, 39Microbia l Risk Assessment Guideline Page 125

Draft

– Do not cite, circulate, or quote change in the average dose value may be satisfactory (e.g., measuring conditional 1

expected dose). In other cases, the change in the variability of doses may be of interest. 2

If sensitivity is measured using analysis of variance techniques, then changes in the 3

average and variance can be assessed together (Frey et al., 2004). Correlation or other 4

quantitative measures of associatio n (e.g., s pider plots, tornado charts ) are commonly 5

available in commercial software packages used for Monte Carlo simulation (Vose, 6

2008 )

. 7

8

To proceed with sensitivity analysis, it is important that the objective of the 9

analysis is clear to the analyst. This objective can be informed by the overall purpose of 10

the risk assessment. Sensitivity analysis for its own sake is rarely rewarding when 11

applied to complex models and often a frustratingly inefficient use of the analyst‘s 12

resources. For a focused exp osure assessment, with specific purposes and scope, it is 13

likely that focused analysis on specific components‘ importance and influence (examined 14

in multiple ways) may be more useful for risk managers. 15

16

The major challenge for sensitivity analysis is that it is difficult to separate 17

sensitivity from uncertainty in most exposure assessment models. Uncertainty about 18

model components can result in a very mixed description of the importance of model 19

components. Theoretically, a model input may be highly influ ential across part of its 20

uncertainty range but much less influential across another part. At the least, 21

acknowledgement of such discrepancies should be communicated to risk managers. 22

Again, focused sensitivity analysis facilitates deeper analysis of a f ew components 23

instead of superficial analysis of many components. 24

25

5.3.4 What is an Uncertainty Analysis? 26

27

The goal of uncertainty analysis is to identify those model inputs whose 28

uncertainty subst antially contributes to the total uncertainty about exposures implied by 29

the exposure model (see Section 6.7) . Uncertainty is a lack of knowledge; therefore, 30

uncertainty can only be reduced via accumulation of new knowledge. Uncertainty 31

analysis suggests where to focus future data gathering efforts and/or scientific research. 32

Like sensitivity analysis, there is no standard method for conducting uncertainty analysis. 33

34

Although the objective of uncertainty analysis differs from sensitivity analysis, 35

the re sults of uncertainty analysis are usually not independent of the results of sensitivity 36

analysis. If uncertainty about an input contributes substantial uncertainty about the 37

model‘s results, then that input usually is also likely to be identified as highl y influential 38

through sensitivity analysis. 39

40

Random variables and parameters in an exposure model are subject to parameter 41

uncertainty (Morgan and He n rion, 1990). This source of uncertainty refers to sampling 42

and measurement errors that are inherent to em pirical data. Statistical techniques may be 43

available to quantify parameter uncertainty. 44

45Page 126 Microbia l Risk Assessment Guideline

Uncertainty about model structure is another source of uncertainty that may be 1

propagated through an exposure assessment. This source may refer to use of surrogate 2

variables, model simplifications or alternative specifications of the model processes. An 3

example of the latter reference might be uncertainty about whether to include a process 4

(e.g., cross

- contamination) in an exposure model. It is often difficult to q uantify the 5

magnitude of uncertainty about model structure. Nevertheless, such uncertainty can 6

substantially alter exposure predictions from a model. Uncertainty associated with model 7

specification can be investigated by testing the differences in fit an d predictions of 8

multiple model forms. If the models are of the same general form (e.g., exponential 9

family), then the effects of including or excluding covariates can be evaluated using 10

likelihood

- based criteria such as the Akaike information criterion. 11

12

If economic information is combined with uncertainty analysis, a value of 13

information analysis approach may yield insights relevant to the goal of uncertainty 14

analysis. Yet, these methods have rarely been employed in microbial exposure 15

assessment. For a simple dichotomous decision, value of information methods can 16

evaluate the economic returns of hypothetical new information relative to the choice 17

made prior to acquiring the new data. These methods highlight an important point about 18

new information; i f additional data will not change a decision, then those data are not 19

valuable. Therefore, risk managers can acknowledge that their decisions hinge on the 20

degree of uncertainty about results from a model. Newly acquired data will presumably 21

reduce that u ncertainty, but its value may be naught if risk managers were not influenced 22

by the magnitude of uncertainty about the original results. 23

24

The techniques for uncertainty analysis are similar to those for sensitivity 25

analysis. A factorial design for uncerta inty analysis enables evaluation of how alternative 26

values for uncertain model inputs influence the model results. For example, if there are k 27

uncertain inputs and two realistically extreme values (i.e., high and low) for each input 28

are proposed, then the model can be calculated 2 k times to examine the influence of each 29

input‘s uncertainty on the results (ECSSC, 2003) . This approach supports examining 30

interactions between the inputs. For example, one extreme value of one input may 31

slightly influence the model‘s results, but when the model is calculated with that extreme 32

value and certain other extreme values of other inputs, the magnitude of its influence 33

substantially increases. 34

35

Analysis of uncertainty is daunting if the number of uncertain parameters o r 36

alternative model structures is large. A simple, univariate alternative to the 2 k factorial 37

design is to calculate results for each of the two extreme values of each input 38

independently; this only requires 2k re

- calculations of the model (ECSCC, 2003). The 39

predicted change in results for each input can be graphically displayed to demonstrate 40

their relative influence on the model‘s results. 41

42Microbia l Risk Assessment Guideline Page 127

Draft

– Do not cite, circulate, or quote 5.4 What Can I Put Into an Exposure Assessment Report? 1

2

Communication of exposure assessment results is challenging because the 3

intended audience is usually diverse. A presentation of these results has to satisfy 4

technical specialists as well as those who are less specialized in quantitative m ethods 5

(ECSCC 2003 , WHO / FAO, 2008) . Consequently, a balance between technical details 6

and general conclusions of the analysis is a goal of any communication (see Chapter 8)

. 7

8

The credibility of an exposure assessment is enhanced through peer

- review and 9

fe edback from public outreach. If communication of the exposure assessment is 10

reasonably transparent and balanced, reviewers can focus their attention on the merits of 11

the analysis and contribute to improving its accuracy and validity. 12

13

The output of an exp osure assessment is usually an exposure distribution. 14

Thoughtful contemplation of the best formats for communicating this distribution and its 15

uncertainty should precede any final decision. A number of graphical formats for 16

presenting risk r esults are av ailable (Vose, 2008 ). Tabulated results can be more useful 17

in some cases and both table and figures may be needed in other cases. A limited number 18

of moments (e.g., mean, variance, skewness) along with meaningful quantiles of this 19

output are routinely pr ovided in a report. You can consider different preferences for data 20

display when selecting the best format. All tables, graphs, and other figures should have 21

clear, concise narrative text to help the reader understand what is being presented. 22

23

If specifi c processes included in the exposure assessment are the subjects of risk 24

management choices, then you should summarize outputs from these processes in the 25

report. Plots of the central tendencies of microbe counts that illustrate trends across the 26

breadth of a model can be useful for some readers. 27

28

To clearly communicate the scenarios considered in the exposure assessment, you 29

can illustrate diagrammatically the conceptual model in the report. A well

- annotated 30

conceptual model will enhance reader‘s underst anding of the analytic approach taken. 31

32

You can list all inputs used in the model and clearly define their reference symbol 33

and name, describe their purpose in the model, provide the probability distribution name 34

and parameter values for random variables , and explain how you handled uncertainty for 35

variables and parameters. You can communicate i mportant inputs by using graphical 36

depictions of their distributions. 37

38

I nclude the mathematical structure of the model transparently, but concisely, in 39

the report. Because it will only be useful to a specialized audience, it is usually 40

appropriate to place the mathematics in an appendix. Nevertheless, this mathematical 41

description of the model will provide the greatest opportunity for clearly explaining the 42

exposu re assessment to the specialized audience. This is the audience that can verify the 43

validity of the model or identify errors in its logic or assumptions. 44

45Page 128 Microbia l Risk Assessment Guideline

Inclusion of a discussion of important assumptions is necessary . A transparent 1

treatment of assump tions improves the reader‘s understanding of the analysis; although 2

some might disagree with assumptions made, it is much preferred that they understand 3

the reasons for the assumptions. Models are always imperfect depictions of reality and 4

their results a re always conditioned on assumptions. The strength of a model, then, is 5

based on the strength of the justifications of its assumptions. 6

7

Presentation of the r esults of sensitivity and uncertainty analyses in tabular or 8

graphical formats is also helpful an d may be required depending on the use of the risk 9

assessment . It is crucial that such presentations are meaningful to both a specialized and 10

non

- specialized audience. Complex analyses that do not illuminate important 11

conclusions will create confusion fo r the reader. 12

13

Exposure assessments can generate large volumes of analytic output. 14

Nevertheless, presentation of the exposure assessment requires deliberation by the 15

analyst; the reader of a report expects that care is taken in what is presented and how it is 16

presented. Avoid a rbitrary deci sions about the content of the report. 17

18

5.5 What are Possible Future Developments in Exposure Assessment? 19

20

The discipline of microb ial exposure assessment continues to grow and evolve. 21

Compared to more established analytic fields like economics, epidemiology or statistics, 22

microbial exposure assessment is still relatively new. Therefore, it is expected that 23

methods and approaches wi ll continue to improve. 24

25

One technique of increasing interest for exposure assessment is the use of MCMC 26

simulation (Gelman et al., 2004). The MCMC method is based on Bayes Theorem, but it 27

is designed to solve problems that would normally be intractable u sing standard Monte 28

Carlo approaches. Using MCMC methods, ― prior distributions ‖ are specified for model 29

inputs and the empiric evidence is integrated with the model to determine which 30

combinations of model inputs best describe

— in a probabilistic sense

— the empiric 31

evidence

. 32

33

Many exposure assessments are generated to guide government policy 34

development. Traditionally, risk assessments are conducted independent of economic 35

analyses, but risk assessment products are commonly incorporated into economic 36

anal yses conducted to support government policy (Williams and Thompson, 2004). In 37

the future, it is expected that economic analysis will be integrated into the risk assessment 38

to provide more accurate and useful policy information. 39

40

Within exposure assessme nt models, costs and tradeoffs between alternative 41

processes could be incorporated so that their effects on the model‘s results were explicitly 42

calculated. For example, it might be the case that a process with variable effectiveness is 43

associated with ext raordinarily high marginal costs at very high levels of effectiveness, 44

but low or absent costs at moderate or low levels of effectiveness. To fully appreciate 45Microbia l Risk Assessment Guideline Page 129

Draft

– Do not cite, circulate, or quote such phenomena, the exposure model needs to incorporate the economic factors. 1

Otherwise the eff

ects of these economic factors may not be understood by risk managers. 2

3

As mentioned for uncertainty analysis, VOI methods are very useful for 4

determining economically

- efficient future data gathering efforts (Yakota and Thompson, 5

2003; Disney and Peters, 2 003). In the future, it is expected that these methods will 6

become standard for exposure and risk assessment analyses. 7

8

Exposure assessment models can serve as templates for the incorporation of better 9

data when it becomes available. In the future, exp osure assessments may be updated with 10

data collected explicitly for the purpose of exposure assessment. Traditionally, exposure 11

assessments use data generated for other purposes. Risk assessors then must struggle 12

with incorporating such data into an expo sure assessment. If data are collected for an 13

exposure assessment, however, it is likely that the data will be better structured for that 14

purpose. The result of using data that is ―fit for purpose‖ is improved exposure estimates 15

for risk management decis ions. 16

17

M RA deal s with many different types of problems; there are a number of 18

approaches and methods that may provide satisfactory solutions. Nevertheless, exposure 19

assessment will continue to evolve towards standard approaches and methods that 20

represent accepted defaults for certain types of problems. Increasing academic attention 21

and scrutiny of exposure assessment will almost certainly bring greater consistency in 22

methods. Because the field of MRA is still emerging, it is also critical that risk asses sors 23

search for reasonable precedents in methodology when embarking on a new exposure 24

assessment project. If precedents are adopted and improved, standardization of methods 25

and models can eventually occur. 26

27

I nternational groups such as the WHO/ FAO and Cod ex Alimentarius will 28

continue to provoke thoughtful discussions about the most appropriate exposure 29

assessment methods and approaches. Such discussions will contribute to improved 30

standardization of exposure assessments in the future. 31

32Page 130 Microbia l Risk Assessment Guideline



## 6. RISK CHARACTERI ZATION 1

2

As introduced in Sec tion 1.1, risk characterization is one of the four fundamental 3

components of risk assessment. Risk ch aracterization is the integrating component of the 4

risk assessment process that ― characteriz es‖ or describes and summarizes microbial 5

health risks. It is the final integrative step of risk assessment. This chapter addresses 6

what a risk assessor can do to take the pertinent elements from the previous components 7

of the risk assessment (hazard identification, hazard characterization, dose

- response, and 8

exposure assessment) and integrate them into a coherent, understandable, and informative 9

conclusion that is useful for decision makers and stakeholders. Further, the risk 10

characterizatio n discusses scenario, model, parameter, data, and analysis options that risk 11

managers should understand and consider when interpreting the results of the risk 12

assessment. The risk characterization, in a sense, brings to full circle the initial plan ning 13

an d scop ing for the risk assessment described in Chapter 2. The content of the risk 14

characterization is intended to reflect the issues and questions laid out during planning 15

and scoping. 16

17

For further detail and discussion on risk characterization, good refe rences are the 18

NRC reports (NRC, 1983, 1994, 1996, 200 9 ), EPA‘s Risk Characterization Handbook 19

(EPA, 2000a) , and An Examination of EPA Risk Assessment Principles and Practices 20

(EPA, 2004b) . This chapter is not intended to provide the level of detail about risk 21

characterization provided in the previous references, but rather provides the microbial 22

risk assessor with guidance on what information to include and how to integrate the 23

information from the previous three chapters. 24

25

6.1 What is Risk Characterizati on? 26

27

In its most general sense, risk is the possibility (and if estimated, probability) of 28

suffering harm. For the purposes of MRA , hazard may be causal or associated with 29

adverse outcome as a represe ntation of intrinsic effects expressed by a microbe. Risk 30

contains elements of both hazard and exposure. Thus, risk is generally understood to be 31

the integration of intrinsic effects, represented by Hazard, and the values for Exposure. It 32

is usually rep resented by some form of the equation: 33

34

Risk = 

## ƒ

Hazard • 

## ƒ

Exposure (Eq. 6.1 ) 35

36

Hazard identification allows you to select and focus on specific features of subject 37

organisms associated with the potential to cause harm. Exposure analysis provides a 38

description of the routes and an estimate of the degree to which a host may be exposed. 39

None of these components can stand alone to characterize risk. When combined with 40

host factors in an evaluation of dose

- response, one can get a quantitative Hazard 41

Ch aracterization of that potential once a host is exposed. Risk characterization takes the 42

specific identified hazards, examines the probabilities of their existence under specific 43

exposure scenarios, and combines those probabilities with those of the likel ihood that the 44

agent will encounter the host in sufficient quantity to cause an effect. 45

46Microbia l Risk Assessment Guideline Page 131

Draft

– Do not cite, circulate, or quote Risk characterization is the final step of the MRA process in which all preceding 1

data collection and analyses are combined to convey the overall conclusions about 2

po tential risk to humans. During risk characterization, the results of the iterative risk 3

assessment process are integrated and documented in a descriptive risk characterization 4

summary. Risk characterization communicates the key findings and the strengths and 5

weaknesses of the assessment through a conscious and deliberate transparent effort to 6

bring all the important considerations about risk into an integrated analysis by being 7

objective, transparent, clear, consistent, and reasonable ( OMB, 2007b; EPA 200 2b; EPA 8

2000a). For these reasons, the risk characterization needs to be complete, informative, 9

and useful for decision

- makers . Therefore, this section of the risk assessment needs to be 10

both sufficiently technical to be scientifically accurate, taking i nto consideration the 11

uncertainties and reporting the assumptions but also co mprehensible by an educated lay 12

audience. As noted below t his is the component that most directly leads to a 13

regulatory/management decision and serves as communication tool for s takeholders. 14

15

Risk characterization describes the ways in which exposure and dose

- response 16

(quantitative) or exposure and hazard assessments (qualitative) are used together to 17

formulate a statement of risk. Risk characterization can be quantitative, whe n values are 18

available for all terms in the risk equation, or it may be semiquantitative, when only some 19

values are available. In many cases, default values/assumptions based on known 20

conditions are used in place of measured ones. Further, when the data are not useful to 21

present a quantitative estimation of risk, then a qualitative description of the risk may be 22

all that can be presented in a risk characterization , which may be sufficient in certain 23

cases. Regardless of quantitative versus qualitative, t he risk characterization should 24

address the risk management questions posed in the planning and scoping phase (and any 25

questions that may have been added or revised during the assessment itself). 26

27

Moreover, risk characterization forms the starting point fo r formulating risk 28

management considerations and provides a foundation for (regulatory) decision

- making

. 29

It characterizes both quantitative data and qualitative information in technical and non

30

technical terms, explaining the extent and weight of evidence , results, and major points of 31

interpretation and rationale. It also summarizes the strengths and weaknesses of the 32

evidence, conclusions, uncertainties, variability, potential impact of alternative 33

assumptions, and discusses scenario, model, parameter, a nd analysis options that may 34

deserve further consideration as the results from the assessment are subsequently used for 35

decision making purposes. 36

37

As an example of how one agency views risk characterization, EPA‘s Policy 38

Statement on risk characterization is as follows: 39

40

Each risk assessment prepared in support of decision

- making at EPA 41

should include a risk characterization that follows the principles and 42

reflects the values outlined in this policy. A risk characterization should 43

be prepared in a manner that is clear, transparent, reasonable and 44

consistent with other risk characterizations of similar scope prepared 45

across programs in the Agency. Further, discussion of risk in all EPA 46Page 132 Microbia l Risk Assessment Guideline

reports, presentations, decision packages, and other documents should b e 1

substantively consistent with the risk characterization. The nature of the 2

risk characterization will depend upon the information available, the 3

regulatory application of the risk information, and the resources 4

(including time) available. In all cases, however, the assessment should 5

identify and discuss all the major issues associated with determining the 6

nature and extent of the risk and provide commentary on any constraints 7

limiting fuller exposition. (EPA, 2000 a) 8

9

6.2 What are the Elements in a Risk Characterization? 10

11

During the risk assessment process, you should have identified areas where policy 12

options were considered, where management decisions and assumptions were made, an d 13

where uncertainties are important. The point of risk characterization is not to reiterate the 14

details of each chapter of the risk assessment, but rather to integrate those chapters to 15

arrive at the risk assessment output (e.g. risk estimate, risk rankin g, or other output), 16

describe the relevant findings, cross

- reference the exposure and dose

- response 17

assumptions (e.g., do the age grouping in exposure assessment and dose

- response 18

assessment match), and discuss other salient elements (as described below). 19

20

Risk characterization consists of two principal steps

— risk estimation and risk 21

description. Risk estimation is the compilation of the types and magnitude of effects 22

anticipated from exposure to the microbe or medium and can be qualitative or 23

quantitati ve depending on the data and methods used. The risk estimation is derived 24

from the output components of the risk assessment (hazard identification, hazard 25

characterization, exposure assessment, and dose

- response analysis). Specifically, the 26

results from the characterization of exposure can be expressed as the number of 27

organisms to which an individual is exposed in a defined amount of time and/or for a 28

certain consumption rate. Resultant estimates of the potential for adverse human health 29

effects can be expressed as an individual risk estimate (e.g., 1 per 1000 probability of 30

illness) or as a population level risk estimate (100 illnesses per year in a region with a 31

population of 100,000 individuals). As described in further detail below, the risk 32

estimat ion can also be modeled to consider time

- dependent elements such as secondary 33

(person

- to

- person) transmission, host immunity, and multiple routes of exposure (ILSI, 34

2000). 35

36

The second component of risk characterization, risk description puts the risk 37

est imation into context by summarizing the event of interest according to its nature, 38

severity, and consequences and discussing and quantifying (to the extent possible) (1) the 39

uncertainties associated with the key components within the risk characterization; (2) the 40

variability associated with key inputs to the model(s); (3) the c onfidence in the resulting 41

risk estimate s through a weight of evidence discussion ; (4) the limitations of the analysis; 42

(5) the critical assumptions; and (6 ) the plausibility of the results. Clearly, your 43

professional judgment will also be necessary to determine what should be included in the 44

risk characterization. You can consider the following elements in risk characterization 45

(adapted from the EPA Risk Characterization Handbook

, EPA 2000a): 46Microbia l Risk Assessment Guideline Page 133

Draft

– Do not cite, circulate, or quote 1

a) Key information

– Consider 1) the studies available and how robust they are; 2) 2

the major risk estimates you calculated, the assumptions and the extrapolations 3

made during the estimated risk calculation, and the residual uncertainties; 3) the 4

use of default parameter values, policy choices and risk management decisions 5

made, if any; 4) whether the key data used for the assessment are considered 6

experimental, state

- of

- the art, or generally accepted scientific knowledge; and 5) 7

variability. 8

9

b) Cont ext

– Consider 1) how you are addressing the risk management questions; 2) 10

how the estimated risk from this microbial hazard compares to other estimates for 11

this hazard, if available. Include discussion of regulatory requirements or if there 12

are regulator y values to consider. 13

14

c) Sensitive Populations

– Consider 1) the range of people that may be affected, 15

including innately susceptible populations (e.g., ethnic groups, gender, 16

socioeconomic and/or nutritional status, other genetic predisposition) and those 17

t hat are highly exposed; 2) a quantitative characterization for each sensitive 18

population may not be necessary or possible. For example, where there are many 19

sensitive population groups for a given microbial hazard, it may be sufficient to 20

estimate risks f or the most sensitive group, and then assume that as long as that 21

group is protected, other groups may be protected adequately. If the quantitative 22

portion of the risk assessment is strongest for the general population due to data 23

availability, then some data

- ba sed adjustment for sensitive populations may be 24

considered. Both results can be presented and discussed. 25

26

d) Life Stages

– Consider the age groupings evaluated and note any life stages that 27

may have particular vulnerability due to behaviors or situati ons that influence 28

exposure patterns and/or innate susceptibilities. For microbial hazards with only 29

short term effects the different life stage may be treated as sensitive populations. 30

However

, if any longer term effects (e.g., health endpoints that spa n a 70 year 31

life) are of interest then life stages may need to be considered differently than 32

sensitive populations. For example, everyone in the general population passes 33

through childhood life stages and exposures to pathogens could vary at different 34

ch ildhood life stages. Thus, depending on the scope of the risk assessment, 35

consideration of childhood life stages may be necessary as risk estimates may 36

vary for the range of important life stages. 37

38

e) Scientific Assumptions

– Describe, 1) where key data gaps exist; 2) what the key 39

assumptions you used during your assessment are and the impact they may have 40

on the assessment outcome. Also note if there is precedent in other risk 41

assessments for the approach or assumptions employed, and note the justifications 42

for selection of any default parameter values that are used. 43

44

f) Policy Choices

– Describe, 1) if your office has different policies about how to 45

assess risk (e.g., different uncertainty factors or different levels of regulatory 46Page 134 Microbia l Risk Assessment Guideline

concern); 2) any policy choic es that may boun d the scope of the assessment. If 1

appropriate, include discussion of consistency with other Agency approaches or 2

decisions. 3

4

g) Variability

– Describe 1) how variability arises from true heterogeneity in 5

characteristics such as dose

- response differences within a population, or 6

differences in microbial levels in the environment; 2) if the values of some 7

variables used in an assessment can change with time and space, or across the 8

population whose exposure is being estimated. The discussion of variability 9

should link to the discussion of assumptions, because variability considerations 10

may be lost or assumed when values for parameters are selected. This element is 11

critical and discussed at length in the previous chapters. An inadequate 12

discussi on of variability can result in a loss of transparency or in the worse case a 13

misleading risk assessment. 14

15

h) Uncertainty

– Describe 1) what uncertainties exist in the assessment (e.g., 16

measurement uncertainty, model uncertainty, uncertainty due to data gaps) ; 2) 17

how you addressed uncertainty (e.g., uncertainty analysis, sensitivity analysis); 3) 18

what impact reducing scientific uncertainties could have on your assessment; 4) 19

where possible, quantitative uncertainty analyses of the data should be presented; 20

at a minimum, a qualitative discussion of the important uncertainties should be 21

provided. This element is critical and discussed at length in the previous chapters 22

(additional references: Morgan and Henrion, 1990; Frey et al., 2004). As with 23

variability, an inadequate discussion of uncertainty can result in a loss of 24

transparency or in the worse case a misleading risk assessment. 25

26

In practice it is often difficult to separate variability and uncertainty, because 27

many uncertainties are in the area of charac terization of variability (e.g. lack of 28

data for certain conditions). In cases where there is uncertainty about variability, 29

be sure to be clear about your use of the two terms. 30

31

i) Bias and Perspective

– Consider 1) that in the light of uncertainty and defa ult 32

choices, your agency may proceed in the direction of more public health 33

protection compared to less protection. If this is the case, this perspective needs 34

explicit explanation; 2) it is not always clear where agency/public health bias 35

enters into you r agency risk assessment

- to the extent it may make a difference in 36

the outcome of your assessment, highlight the potential bias so the impact will not 37

be overlooked or misinterpreted by the risk manager. For example explain the 38

implications of selecting a 50 th versus 95 th percentile in a data set. 39

40

j) Strengths and Weaknesses

– Throughout the risk characterization you should 41

highlight and/or describe 1) major imbalances among the components of the 42

assessment, e.g., the case for the microbe posing a hazard m ay be strong, while 43

the overall assessment of risk is weak because there are no data about whether 44

there is exposure to the microbe; 2) the data/information that present the strongest 45

evidence for your conclusions as well as the data that may not be as str ong 46Microbia l Risk Assessment Guideline Page 135

Draft

– Do not cite, circulate, or quote (weakness) and how you incorporated that into your conclusions. You should 1

also discuss the quality of the data used and how the data quality pertains to 2

variability and uncertainty. 3

4

k) Key Conclusions

– Describe 1) the key points that need to be commun icated for 5

knowledgeable interpretation of the risk assessment; 2) that small subset of key 6

findings supporting information (strengths and weaknesses, results from 7

sensitivity/uncertainty analyses) that really makes a difference in the assessment 8

outcome. 9

10

l) Alternatives Considered

– Consider 1) if there are plausible alternatives to the 11

risk estimated in your assessment and how you dealt with those alternatives (e.g., 12

alternative models that could be used, different hazard pathways); 2) the 13

limitations of ma king comparisons among the alternatives; 3) where appropriate, 14

how your conclusion about risk compares to other possible risks. If other risks are 15

compared, the discussion should highlight the limitations of such comparisons as 16

well as the relevance of the comparisons 17

18

m) Research Needs

– Describe the key data needs and/or methodology gaps that 19

were identified during the course of the risk assessment. 20

21

Each element described above is important to address in a risk characterization; however, 22

no single element is necessarily more ―critical‖ than others. As the risk assessor, you 23

need to be aware of all these elements and address them appropriately in the risk 24

characterization. For each element you considered describe the data you found, or i f you 25

found no data or information for a particular element, or you used a default assumption 26

that also needs to be stated. 27

28

6.3 How Do I Prepare a Risk Characterization? 29

30

Because the purposes of MRA vary, th e mechanisms of risk characterization, 31

including risk integration, can also be quite varied. Some organizations prefer to attempt 32

separate hazard and exposure characterizations, and combine the results once all the 33

pieces are described and quantified. Oth ers find that it is valuable to have feedback from 34

the major assessment components throughout the assessment timeframe, and thus have 35

some elements of risk integration running simultaneously with the component 36

assessments. Hybrids of these approaches are possible, as well. Your selection of an 37

approach to characterization and integration may be driven by institutional practices, 38

completeness of data, and/or timing of availability of data

. 39

40

It is common to have an iterative process in which a simple scopin g 41

characterization (e.g., risk profile) is generated early in the process. This allows the 42

assessors to identify data gaps, to recommend additional data generation or gathering, or 43

to use default assumptions as a starting point to evaluate the potential i mplications of 44

scenario being evaluated. Later iterations may adjust the scope of the characterization by 45

focusing in on the most important hazards or exposures identified in the initial iterations, 46Page 136 Microbia l Risk Assessment Guideline

or expand as needed when additional hazard or exposure e lements are identified or 1

additional data are provided. 2

3

As discussed in S ection 2.7 and Chapter 7 , there are many different levels of 4

decisions that need to be made during risk assessment. As the assessor, you take 5

responsibility for decisions involving scientific judgment. Other decisions that are 6

ultimately policy calls, are informed by science, but will most likely be made by risk 7

managers. Risk managers may want to evaluate policy decisions and make revisions 8

during different iterations of the risk assessment. Any changes in policy decisions or 9

scope can be tracked for transparency. It is unlikely that scientific judgments will change 10

unless more data become available or compelling scientific arguments are made during 11

internal or external peer revi ew. It is not unusual for risk managers to adjust policy 12

decisions or refine the scope or questions for the risk assessment during the risk 13

assessment process. In many ways, this process highlights the importance of the iterative 14

nature of risk assessmen t. You should be prepared for these types of changes and provide 15

clear documentation to help delineate scientific judgment from policy decisions. Very 16

often something that could be scientific judgment if data were available becomes a policy 17

d ecision when data are lacking. Determining the threshold between those two situations 18

can be unclear. 19

20

Some of the driving forces for this level of variation in performing risk 21

characterization are available resources, time constraints, legal requirements

, and Agency 22

culture. In some cases, a first attempt at a complete risk characterization, in the sense 23

that all evaluation elements are accounted for to at least a limited extent, is desirable to 24

provide managers with an initial scoping of likely outcomes before all information has 25

been fully evaluated

. Sometimes, certain factors are given precedence at the outset of an 26

assessment, such as a particular exposure scenario, and interchange between the hazard 27

and exposure components can take place throughout the course o f an assessment. This is 28

generally the case when examination of a specific health effect or exposure model is the 29

driving force behind the risk assessment. In other cases, it may be necessary to isolate 30

the hazard and exposure components to avoid results from one having an undue influence 31

on the assessment of the other. This may happen if the type of assessment is intentionally 32

broad and the desire is to not eliminate scenarios, or emphasize specific effects until a 33

first pass is made. This can result i n an iterative assessment, with feedback among the 34

components occurring during later iterations. 35

36

In selecting an approach for risk characterization, be careful to ensure that any 37

simplifying assumptions that are employed are in fact appropriate and clearl y identified

. 38

Within that context, and to the extent possible, you should demand higher quality input 39

data and fewer simplifying assumptions when seeking increased accuracy and precision 40

from your risk assessment. F rom a modeling perspective, biological ―realism‖ is often 41

counter

- balanced by analytical or computational complexity. The increase in the 42

complexity of a model structure can increase variability and/or uncertainty due to 43

increased needs associated with model specification (EPA, 2004c). On the other hand, a 44

simpler model involves implicit or explicit assumptions that may or may not be realistic 45

or appropriate for a particular situation. M ore complex models should be considered or 46Microbia l Risk Assessment Guideline Page 137

Draft

– Do not cite, circulate, or quote used under conditions in which the added complexity may provide sufficient additional 1

insight that the additional complexity is warranted (King et al., 2008; Soller and 2

Eisenberg, 2008). As discussed in Chapter 5, statistical methods, such as use of the 3

Akaike Information Criterion or similar likelihood

- based measures , to judge the desirable 4

level of complexity in statistical models. Representative MRA model forms are 5

discussed in Section 6.5.1. 6

7

6.4 Are All Risk Characterizations Quantitative, and What Do I Do When 8

Quantitative Data are Unavailable for Some Elements of the Risk 9

Characterization? 10

11

While many assessments are quantitative, risk charact erization can also be 12

qualitative and/or descriptive. It is also possible that some parts of the analysis can be 13

quantitative, while only approximations and/or defaults are possible for other elements 14

(this is sometime referred to as a semi

- quantitative a nalysis). 15

16

Although ―risk‖ is often thought to imply a probability of an adverse health effect, 17

and therefore thought of as quantitative, some assessments can only be expected to 18

approximate risk probabilities. In certain cases, the risk characterizatio n can at best be 19

thought of as a screening exercise, providing only a sense of whether the risk might be 20

judged high, medium, or low . Sometimes one can obtain values for some of the 21

components of hazard and exposure characterization, but not all. In thes e cases

, it may 22

be possible to utilize default values for the missing data elements to provide enough 23

information to establish limits of risk for the organism and conditions in question. A 24

bounding analysis may be appropriate. For example, you can do a d eterministic analysis 25

with plausibly conservative values for the unknown parameters and see if the resultant 26

risk is above the level of concern. If it is not above the level of concern no further data 27

on these parameters is probably necessary. If the ris k is above the level of concern, then 28

you have found out that gathering more data on the unknown parameter value(s) would 29

be justified. 30

31

Finally, another type of assessment that can be qualitative is a relative risk 32

assessment. These types of assessment have been valuable for numerous agencies, 33

especially for evaluating the potential benefits of management actions (treatments) or 34

alternatives in conditions where rigorous and quantitative data were not available. 35

36

6. 5 Are There Different Forms of Risk Char acterization? When Do I Apply 37

Them? 38

39

Risk characterization should be complete, informative, and useful for decision

40

makers . The appropriate level of detail for any particular assessment will be a function of 41

the goals of the assessment, the questions that the assessment are intended to answer, and 42

the data that are available to conduct the assessment. Whether or not a particular level of 43

detail is appropriat e for a particular situation will depend on the purpose of the 44

assessment. 45Page 138 Microbia l Risk Assessment Guideline

When hazards are well defined and specific exposure scenarios allow accurate 1

exposure and dose

- response calculations, a quantitative risk characterization may be in 2

order. This is generally the case with event

- driven retrospective assessments. 3

Prospective assessments often lack sufficient information and data to make the 4

appropriate calculations for detailed quantitative assessments. In some cases, default 5

assumptions and/or param eter values can substitute for measured values so that 6

calculations can be made, but with considerable uncertainty. Qualitative risk 7

characterization may be useful, when risk management choices need to be made and a 8

general sense of risk is all that is re quired by managers. For example, if default values, 9

coupled with an understanding of the uncertainty that accompanies their use, can enable 10

completion of a semi

- quantitative assessment that gives enough information for decision 11

making, then that assessmen t is more useful than the failure to produce any assessment, 12

due to the inability to cope with the lack of data for which the default assumptions 13

substitute. Finally, the appropriate form of risk characterization may change during the 14

risk assessment, if questions to be answered change or if additional data become 15

available. 16

17

T he availability of data and the appropriate type of risk characterization are 18

generally related. Assessments of specific events can sometimes result in precise 19

calculations of expo sure and dose

- response, assuming the agent is well identified. In 20

some prospective analyses, estimates of organism concentrations in a particular source 21

may be easier to estimate than the actual exposure of particular populations. In these 22

types of cases , default values may need to be substituted for accurate population 23

estimates. Similarly, dose

- response relationships are only available for a limited range of 24

organisms (refer to dose

- response chapter for a list of available dose response 25

relationships), so quantitative assessments can only be conducted for those microbes that 26

have known (or derivable) dose

- response relationships. 27

28

With respect to quantitative risk characterization, you can employ a variety of 29

model forms for the assessment of infectious disease transmission and the potential 30

impact or benefit of intervention efforts/management actions. Particular characteristics 31

of each model form allow for the capture of different aspects of the disease transmission 32

system (EPA, 2004 b ). The two most co mmonly employed classes of MRA models are 33

static and dynamic models. An overview of these types of models along with pros and 34

cons of each is provided by Soller and Eisenberg (2008). Exclusion from the following 35

discussion does not preclude use of a part icular model form; however, justification for 36

use of a particular model form should be included in the risk description. 37

38

For examples of risk characterization within MRA you can refer to Foodrisk.org 39

for a searchable database of MRA s. 40 40

41

40 http://foodrisk.org/risk_analysis/RA/RAs.cfmMicrobia l Risk Assessment Guideline Page 139

Draft

– Do not cite, circulate, or quote 6

. 5 .1 When is a St atic Model Appropriate ? 1

2

A static model would be appropriate in those cases where the central question is 3

concern ed with the probability of infection or illness relative to the dose of pathogens 4

acquired from a single exposure . Such models can handle complex details about the 5

course of events that lead to exposure and infection and can be analyzed by well

6

established statistical techniques that require fewer assumptions than do dynamic models 7

(d iscussed below) . Static models are useful for analyzing situations where the effect of 8

an intervention directed to individuals (e.g., point

- of

- use remediation) is more important 9

than the effect on transmission throughout the population; they are not appro priate for 10

measuring indirect effects at the population level (e.g., the effect of water treatment 11

interventions on risk due to secondary transmission). 12

13

14

15

Figure 6.1 Static Risk Assessment Conceptual Model 16

17

18

Some infectious diseases are not readily transmitted from person to person but are 19

acquired, to the best of current knowledge, only by consumption of or contact with 20

contaminated environmental materials (e.g., L. monocytogenes from food, Naeglaria 21

fowleri infection from water). In ot her cases, although an agent may have the potential to 22

be transmissible, the particular situation is such that the person

- to

- person component is 23

unknown or thought to be negligible . U nderstanding the pattern of human infections 24

from such pathogens or expo sure scenarios may be best achieved through the use of static 25

models (parallel to those used for toxicological risk assessments) (Figure 6.1 )

. 26

27

The se chemical risk assessment

- based models are used to estimate risk at an 28

individual level and typically foc us on estimating the probability of infection or disease 29

to an individual as a result of a single exposure event. With respect to microbial 30

contaminants in the environment or a par ticular media (food, water ) , a fundamental 31

simplifying assumption of static model

- based analysis is that exposure events and 32

infection/disease are independent; that is, the outcome from one exposure event does not 33

affect a subsequent exposure, and one individual‘s outcome has no impact on any other 34

individual‘s outcome. Thus, se condary transmission and immunity are most often 35

assumed to be negligible or are of similar magnitude and effectively cancel each other 36

out. (It is generally assumed that secondary transmission would increase the level of 37

infection/disease in a community relative to a specific exposure to pathogens, and 38Page 140 Microbia l Risk Assessment Guideline

immunity would decrease the level of infection/disease in a community relative to a 1

specific exposure to pathogens.) 2

3

6. 5 .2 When is a Dynamic Model Appropriate ? 4

5

Risk managers and regulators are often concerned with risk on a societal or 6

population scale. Thus, individual risks need to be translated to the level of the exposed 7

population or some other relevant part of that population. When an i nfectious agent that 8

occurs in the environment or a particular media is also contagious, its impact on a 9

population can be significantly influenced by the interactions between contagious and 10

susceptible individuals. To assess the full impact of human expo sure to pathogens, you 11

should consider address ing risk at the population level in addition to individual risk at the 12

dose

- response level. For a thorough evaluation of risks that are manifest at the 13

population level, MRA methods should explore the relative importance of secondary 14

transmission and immunity, and thus capture and integrate the dynamic interplay of hos ts, 15

agents, and environments. 16

17

Dynamic MRA models take two main forms : deterministic or stochastic. 18

―Deterministic‖ means that the model output i s strictly determined by the starting 19

conditions and the values of the parameters in the equations that define the system. In 20

stochastic models, events are treated as stochastic (random) events rather than 21

deterministic ones. Deterministic dynamic MRA mo dels are suitable for large 22

populations of individuals randomly interacting with one another. In this form, the 23

population is divided into epidemiological states such as: (1) susceptible, (2) diseased 24

(infectious and symptomatic), (3) carrier (infected b ut asymptomatic), and (4) immune 25

(partial or complete). Only a portion of the population is in a susceptible state at any 26

point in time, and only those individuals in a susceptible state can become infected 27

through exposure to pathogens. The dynamic aspe ct of the model means that members of 28

the study population move between epidemiological states at different rates, and thus, the 29

number of individuals in each state changes over time. A representative conceptual 30

model for this type of MRA model is present ed in Figure 6.2

. 31

32Microbia l Risk Assessment Guideline Page 141

Draft

– Do not cite, circulate, or quote 1

2

Figure 6.2 Dynamic Risk Assessment Conceptual Model 3

(Source : Soller, 2009 ; Soller and Eisenberg, 2008) 4

5

Deterministic dynamic MRA models are expressed mathematically as a set of 6

differential equations. These equations describe th e rate of change in the number (or 7

density) of individuals in a particular state (or compartment) over time and have defined 8

parameters and starting conditions. Deterministic dynamic MRA models have a number 9

of limitations. If they are used to model rela tively small populations, the assumption of 10

homogeneous mixing of the individuals in the population can lead to mis s

- estimation of 11

disease. These models also require appropriate parameter values for transmission rates 12

and some of this information can be q uite difficult to determine accurately. Lack of 13

knowledge and data, as well as inherent biological variability, suggest a need for 14

uncertainty and sensitivity analyses of parameter values. Furthermore, random events 15

such as local introduction or local di e

- out of a disease in a neighborhood of a 16

heterogeneously mixing population are difficult to incorpora te into these models (EPA, 17

2004 b ). 18

19

In a stochastic form, dynamic models incorporate probabilities at an individual 20

level and are evaluated by an iterativ e process (e.g., susceptible person A has a 21

probability of contacting person B, who has a proba bility of being infectious ). This type 22

of model also uses states for classifying the epidemiological status of the population and 23

subpopulations under study, bu t differs from the deterministic dynamic MRA models in 24

that the compartments contain discrete individuals rather than the numbers or densities of 25

persons that are represented by the compartments in deterministic dynamic MRA models. 26

27

In stochastic dynamic M RA models, events are treated as random (stochastic) 28

events rather than deterministic ones. These models employ distributions of outcomes 29

rather than the average outcomes as do the deterministic models; a stochastic model will 30Page 142 Microbia l Risk Assessment Guideline

produce different results ea ch time it is run. Stochastic forms are suitable for small 1

populations and heterogeneous mixing patterns where stochastic events can have a major 2

impact. In a small population, chance events, such as an infectious person contacting 3

only immune persons du ring the infectious period of illness, may have a substantial 4

impact on the transmission dyn amics of the disease (EPA, 2004 b ). 5

6

Based on this information, risk characterization can be thought of as modular, 7

with different modules requiring specifi c data for calculations. Some modules require 8

detailed data, while others may only require default estimates or descriptive information. 9

Risk management options may obviate the need for precise data on exposure 10

components, if exposure can be limited by specific actions. Thus, the appropriate module 11

(and corresponding level of detail) for any particular assessment will be driven by the 12

goals of the assessment, the questions that the assessment are intended to answer, and the 13

data that are available to conduct th e assessment. 14

15

6.6 How are Sensitivity and Uncertainty Analyses Related to Risk 16

Characterization? 17

18

The discussion in this section is limited to data sen sitivity and uncertainty and 19

does not include sensitivity and uncertainty in overall decision

- making, which may 20

consider decision

- maker judgments and values beyond the risk assessment. 21

22

Although uncertainty and variability are different, in practice it can be difficult to 23

separate the two, particularly when uncertainty about variability is important (Section 24

6.2). It may be practical to characterize uncertainty and variability together if you clearly 25

describe what you are doing. Uncertainty analysis ―is t he computation of the total 26

uncertainty induced in the output by quantified uncertainties in the inputs and models‖ 27

(Morgan and Henrion, 1990). Uncertainty analysis is a key concern for risk managers 28

because it provides information about the overall relia bility of the risk estimates. 29

Measures of model ―uncertainty‖ communicate to risk managers the risk assessor‘s best 30

judgment as to the overall quality of the numerical risk estimates generated by the MRA. 31

Confidence intervals, ―credible ranges‖ developed through Bayesian analyses, and other 32

measures of dispersion in risk should be presented clearly, and their meaning 33

communicated clearly. Simi larly, clear graphical or tabular presentations are very useful. 34

To the extent that intermediate calculations ad d value and understanding to the results, 35

they can also be included. Key assumptions related to model selection, input data, and 36

parameters should be provided and discussed, as well as their implications for the model 37

results and uncertainty. Any conserv ative assumptions that are built into the model 38

should be explained and the impact of using less conservative assumptions should be 39

discussed. 40

41

It is also important to carefully evaluate the impact of known sources of 42

variability in model outputs. This i s generally done through use of one or more forms of 43

sensitivity analysis. Sensitivity analysis can also help determine whether more resources 44

should be put into parameter estimation. Sensitivity analysis ―is the computation of the 45

effect of changes in i nput values or assumptions (including boundaries and model 46Microbia l Risk Assessment Guideline Page 143

Draft

– Do not cite, circulate, or quote functional form) on the outputs‖ (Morgan and Henrion, 1990). Sensitivity analyses 1

techniques range from simply conducting a small number of additional model runs with 2

different parameter values to performing a fully probabilistic evaluation of the effects of 3

variations in parameter values on model outputs. The specific approach that is taken will 4

depend on the nature of the data and models supporting a given assessment. USDA 5

identified several se nsitivity analytical techniques useful for MRA (Frey et al., 2004). 6

The methods evaluated range d from simple and intuitive (varying input values across 7

their observed ranges, scatter plots) to more complex statistical procedures (e.g., 8

classification and regression tree [CART]). For any given risk assessment, it is likely that 9

one or more of these methods will be useful for sensitivity analysis

. 10

11

Although sensitivity analyses are useful for evaluating the effects of the 12

variability in single parameters on risk estimates, when multiple parameter values vary, 13

the results of sensitivity analyses should be interpreted cautiously (E PA, 1997 a ). If the 14

variations in parameter values are independent of one another, it is easy to overestimate 15

the impact of varying more than one value because using upper or lower percentile values 16

for more than one variable can yield point estimates of risk that are overly conservative 17

or insufficiently protective. If the variability in risk parameters is correlated, the impact 18

of their variations may not be easy to estimate using sensitivity analysis. In such cases, a 19

more detailed and comprehensive analysis may be required, usually employing 20

probabilistic approaches such as Monte Carlo or related simulation techniques. Where 21

the variability in model parameters can be partitioned into components mainly reflecting 22

variability and uncertainty, ―two

- dimensional‖ Monte Carlo analysis can be employed to 23

estimate the relative importance of these two components. 24

25

The EPA Exposures Fact ors Handbook (EPA, 1997a) provides several approaches 26

to quantitative uncertainty and sensitivity analysis (Table 6.1): 27

28

29Page 144 Microbia l Risk Assessment Guideline

1

Table 6.1 Approaches to Sensitivity and Uncertainty Analysis Recommended in 2

EPA’s Exposure Factors Handbook (Source: EPA, 1997a) 3

4

A pproach Description Example

Sensitivity analysis Changing one input variable at a time

while leaving others constant to examine

affect on output Fix each input at lower

(then upper) bound while

holding others at nominal

values (e.g., medians)

Analytical

uncertainty

propagation Examining how uncertainty in individual

parameters affects the overall uncertainty

of the exposure assessment Analytically or numerically

obtain a partial derivative of

the exposure equation with

respect to each input

parameter

Pro babilistic

uncertainty analysis Varying each of the input variables over

various values of their respective

probability distributions Assign probability density

function to each parameter;

randomly sample values

from each distribution and

insert them in th e exposure

equation (Monte Carlo

simulation)

Classical statistical

methods Estimating the population exposure

distribution directly, based on measured

values from a representative sample Compute confidence

interval estimates for

various percentiles of the

exposure distribution

5

In addition, Morgan and Henrion (1990) discuss in detail the following four techniques 6

for sensitivity and uncertainty analysis, including: 7

8

a) D eterministic

– O ne

- at

- a

- time analysis of each factor holding all others 9

constant at nomin al values 10

b) D eterministic joint analysis

– C hanging the value of more than one factor at 11

a time 12

c) P arametric analysis

– M oving one or a few inputs across reasonably selected 13

ranges such as from low to high values in order to examine the shape of the 14

response 15

d) P robabilistic analysis

– U sing correlation, rank correlation, regression, or 16

other means to examine how much of the uncertainty in conclusions is 17

attributable to which inputs 18

6. 7 How are Quality of Life Measures Important in MRA ? 19

20

Quality of life measur es are usually included in cost

- effectiveness analyses (CEA) 21

rather than within risk assessment. You should be aware of how your risk assessment 22

results might be used, such as in a CEA . For example, EPA has used quality

- adjusted life 23Microbia l Risk Assessment Guideline Page 145

Draft

– Do not cite, circulate, or quote years (QALY) and Morbidity Inclusive Life Years (MILYs) 41 in the regulatory impact 1

analysis for the Final Clean Air Interstate Rule (EPA, 2005b, Appendix G) and the 2

LT2ESWTR . (EPA, 2006a, Appendix U). 3

4

Qua lity of life captures the impacts that illness has other than medical costs and 5

lost work hours. It is particularly relevant for chronic illnesses that cause pain, suffering, 6

and a sacrifice in lifestyle. One concept, known as QALY , is a method for assig ning a 7

numerical value for quality of life and translating that numerical value to a monetary 8

measure (WHO, 2001). Duration and severity of illness can also be used to characterize 9

quality of life, but these are not expressed in monetary units, so would n ot be utilized in 10

the same manner as QALYs. D isability adjusted life

- years ( DALYs ) are recommended 11

in WHO Water Quality: Guidelines, Standards and Health to integrate the effects of a 12

single agent, compare the health effects of different agents or condit ions, and to inform 13

the debate on acceptable risk (WHO, 2001). WHO expects that ―DALYs will play an 14

important role in prioritizing risk factors, determining levels of acceptable risk, setting 15

health targets and appraising effectiveness [of policy or mitig ation] through examining 16

public health outcome.‖ DALYs and QAL Ys are not calculated in the same manner and 17

have reversed scales of measure. DALYs measure a health gap, with full health 18

represented as 0 and full disability (death) as 1.0; QALYs measure health expectancy, 19

with full health represented as 1.0 and lowest possible health state (death) as 0 ( Airoldi 20

and Morton, 2009 ; Gold et al. , 2002 ; Rice et al.

, 2006). 21

22

It is important to note that QALYs and DALYs are not objective measures and 23

require a de scriptive conceptualization of health states. In addition, there can be 24

significant differences in ranking due to ethnicity, gender, and area of residence (different 25

cities; urban versus rural). Thus, there is much controversy regarding the validity of 26

t hese measures partially because there is no accepted ―gold standard‖ for determining 27

criterion validity (Gold et al., 2002). 28

29

Agencies may have different histories of involving economists in risk assessment. 30

For example, EPA has recognized for many years the need for early involvement of 31

agency economists in risk assessments. EPA‘s primary concern has been that without 32

this early involvement outcome measures from the risk assessment may be of a form that 33

cannot is not useable in cost benefit analysis. I n addition, there is increasing recognition 34

that the risk generation process often involves predictable human response to 35

environmental factors or firms‘ or individuals‘ responses to economic and social 36

conditions. Economists are trained to model and esti mate these kinds of responses. The 37

inclusion of economists on risk assessment teams as scientific analysts, not as managers, 38

could improve the accuracy of risk assessments by allowing risk assessment to 39

incorporate models of production systems and human b ehavior that influence risk levels 40

(Williams and Thompson 2004). 41

42

43

41 MILY combines QALYs saved from avoi ded cases of non

- fatal morbidity with life years resulting from

mortality risk reductions (assigned a weight of 1.0).Page 146 Microbia l Risk Assessment Guideline

6. 8 How Can a Risk Assessment be Validated? 1

2

Validation of an assessment can occur at m ultiple levels (ECSCC 2003, 3

WHO/ FAO 2008) . Validation of the conceptual and mathematical models, the computer 4

algorithm and the assessment‘s pred ictions all occur before a risk assessment can be 5

considered validated. Except for the final level, the validation process is often 6

accomplished through review by persons outside the risk assessment project. 7

8

Model validation and verification in risk assessment are general terms that are 9

sometimes used to refer to rigorous data driven evaluation of models, but more often they 10

are used interchangea bly to refer to a less rigorous ―reality check‖ that may have poorly 11

defined validation criteria. Risk assessors should be aware of the differences between 12

model validation and verification and whether a model has been validated for 13

interpolation or extra polation. Researchers gathering data for the USDA use the more 14

formal rigorous definitions of verification and validation as follows (Oscar, 2005): 15

― Verification

... is the successful outcome of the performance evaluation process where 16

the model predictions were compared with the data used in model development (that is, 17

dependent data). In contrast, validation

... is the successful outcome of the performance 18

evaluation process where model predictions were compared with data that was not used 19

in model developmen t (that is, independent data)

. ‖ 20

21

The iterative nature of most assessments suggests that the model s ha ve been 22

reviewed several times by risk managers and/or experts at the conceptual and 23

mathematical development phases of the project. Nevertheless, public and peer review is 24

usually solicited to examine the results of the assessment. Close scrutiny of the 25

conceptual and mathematical models

– and the computer algorithm

– by specialists 26

knowledgeable in statistics, epidemiology and mathematics will serve to s anction the 27

validity of the mechanics of an assessment model. 28

29

The output of an exposure or dose

- response assessment (a dose

- response 30

relationship, an exposure distribution ) is often not readily measured in nature. 31

Surveillance data may be available for some outputs of the model s and statistical 32

measures of agreement between the model‘s predictions and empirical observations are 33

helpful in describing the accuracy of the model. Creative uses of empiric evidence may 34

serve to support a contention of validi ty. Nevertheless, most risk assessments cannot 35

meet this burden of proof concerning their validity. Such is the nature of many risk 36

assessment problems; their verification primarily stems from the logic and reasoning built 37

into the models used to solve t hem. 38

39

Because validation implies different criteria in different situations, any discussion 40

of validation should refer to how the validation was performed so that readers may 41

understand the degree of rigor the validation effort entailed. One method of val idating 42

the risk assessment findings is to compare the outputs to epidemiological data to 43

determine whether the risk estimates are consistent with reality. The following examples 44

ill ustrate MRA model validations: 45

46Microbia l Risk Assessment Guideline Page 147

Draft

– Do not cite, circulate, or quote a) Rotavirus in Drinking Water: To confirm the validity of the output results of 1

the epidemiologically

- based model used in a case study of rotavirus in drinking 2

water (Soller et al., 1999), a dynamic model was modified using actual data and 3

best judgment to analyze and simulate a 1981 rotavirus out break in the Eagle

- Vail 4

and Avon communities in Colorado (Hopkins et al., 1984). Although a rigorous 5

direct comparison of the results from the actual outbreak and the rotavirus 6

simulation could not be conducted due to a lack of specific surveillance data (e.g., 7

concentration data, secondary spread), a qualitative comparison was made to 8

assess the plausibility of the output from the model. The overall attack rate for 9

diarrhea and/or vomiting during the rotavirus epidemic was reported to be 10

approximately 32 % (Hopkins et al., 1984). Using virus detection or serological 11

methods, it was estimated that a total of approximately 23% of the population 12

became ill from rotavirus exposure during this event. The results of a 5000 trial 13

Monte Carlo simulation of the o utbreak using the model showed that about half of 14

the trials resulted in average daily disease prevalence rates ranging from 7.5% and 15

25%, which compares favorably to the historical estimate of 23%. Thus, it may 16

be inferred that the output from the model seems plausible and intuitively 17

consistent with the actual outbreak data. 18

19

b) Cryptosporidium in Drinking Water: Teunis and Havelaar (1999) conducted a 20

case study of Cryptosporidium in drinking water and discussed the importance of 21

and opportunities to attem pt validation of their calculated estimates of yearly 22

individual infection risk through comparison with actual epidemiological data on 23

endemic/epidemic cryptosporidiosis. Their approach also provides a logical and 24

transparent methodology to integrate qual ity of life

- based approaches into the risk 25

assessment by expressing all health effects in one single metric

— the DALY. 26

Such an approach has the added advantage of not being disease

- specific and lends 27

itself for risk comparisons (e.g., with chemical risks, for economic evaluations). 28

29

Whether or not formal validation is possible, peer review is an important aspect of 30

evaluating model s (OMB, 2004)

. 31

32Page 148 Microbia l Risk Assessment Guideline



## 7. RISK MANAGEMENT 1

2

7.1 What is Risk Management? 3

4

Risk management is a ubiquitous term used in settings as diverse as financial 5

investing, military planning and public health. Within federal public health agencies, risk 6

management refers to activities ranging from high

- level policy making to routine, 7

sometimes pro forma

, risk control applications in operational risk management. This 8

chapter provides an overview of the risk management processes likely to be encountered 9

by microbial risk assessors. 10

11

The NRC ―Red Book‖ initial ly defined risk management in very broad terms as 12

―the process of evaluating alternative regulatory options and selecting among them.‖ 13

(NRC

, 1983). By 1996, a follow

- on NRC committee described the activities of risk 14

managers: 15

16

Risk managers are supposed to deal with broad social, economic, ethical, 17

and political issues in choosing from among a set of decision options by 18

using the results of the risk assessment and their understanding of the 19

other issues. Making tradeoffs, which may be called risk

- benefit , cost

20

benefit, or risk

- risk evaluatio ns, is part of risk management. (NRC, 1996) 21

22

The NRC reports focused on management processes occurring with a single risk 23

assessment. During the mid

- 1990s

, the Presidential/Congressional Commission on Risk 24

Assessment and Risk Management argued that risk management should no longer be 25

thought of a process that focuses on decisions about managing one risk at a time. Rather, 26

governmental agencies need to confront the task of managing risks from multiple hazards 27

and expos ures. To this end, the Commission provided a framework for risk management 28

recommending that the risk management process include steps to (P/CC, 1 997 ): 29

30

a) Formulate the problem in broad context 31

b) Analyze the risks 32

c) Define the options 33

d) Make sound decisions 34

e) Take actions to implement the decisions 35

f) Perform an evaluation of the effectiveness of the actions taken 36

The engineering and systems analysis view of risk management has been 37

described by Haimes (2004): risk management is the process focused on controlling risk s 38

by addressing: 39

40

a) What can be done and what are the options for controlling risks? 41Microbia l Risk Assessment Guideline Page 149

Draft

– Do not cite, circulate, or quote b) What are the trade

- offs in terms of risks, benefits and costs? 1

c) What are the impacts of risk management decisions on future options for risk 2

management? 3

Federal state and loc al public health agencies are charged with the responsibility 4

of preventing, mitigating

, or controlling risks to the public‘s health. As a general 5

concept, it is clear that the ―mission‖ of risk management is accomplished using risk 6

management processes a t several levels. For example, Table 7. 1 describes risk 7

management as a strategic, applied

, or operational function in the agency. The strategic 8

level is concerned with managing the agency‘s portfolio of risks; the applied level

— the 9

primary focus of this G uideline

— concerns the risk management processes surrounding 10

specific risk assessments; and operational risk management deals with risk management 11

that is guided by standard operating procedures. 12

13

Table 7. 1 Classes of Risk Manage ment in Federal Agencies 14

Class of Risk Management Description

Strategic Risk Management (and

Policy Making) Long

- term, broadly based view of the agency‘s entire

risk portfolio. Interface with the public, industry and

governmental stakeholders about polic y issues such

as the level of acceptable risk, the risk

- based

decision

- making process

Applied Risk Management Charters and collaborates with risk assessors on

newly identified or emerging risks, new scenarios for

known risks and risk mitigation scenarios . The

information gained from the risk assessment is used

in risk management decisions about controlling the

risk

Operational Risk Management Implements prescribed administrative, engineering or

other controls to maintain risk at appropriate levels or

be low.

15

The Codex Draft Principles of and Guidelines for the Conduct of Microbial Risk 16

Management includes the following 8 principles (Codex, 2007 a ). 17

18

a) Principle 1: Protection of human health is the primary objective in MRM. 19

20

b) Principle 2: MRM should take in to account the whole food chain. 21

22

c) Principle 3: MRM shou ld follow a structured approach 23

24

d) Principle 4: MRM process should be transparent, consistent and fully 25

documented. 26

27Page 150 Microbia l Risk Assessment Guideline

e) Principle 5: Risk managers should ensure effective consultations with relevant 1

interes ted parties. 2

3

f) Principle 6: Risk managers should ensure effective interaction with risk assessors. 4

5

g) Principle 7: Risk managers should take account of risks resulting from regional 6

differences in hazards in the food chain and regional differences in availabl e risk 7

management options. 8

9

h) Principle 8: MRM decisions should be subject to monitoring and review and, if 10

necessary, revision. 11

12

7.2 When and How Can Risk Managers be Involved in Risk Assessments? 13

14

Risk management begins before risk assessment. Sometimes, the recognition of 15

potential problem and the general hazard identification occurs externally to the agency 16

and is brought to the attention of risk managers by the publi c, stakeholders

, or other 17

governmental organizations. Risk managers typically determine the need for a risk 18

assessment and provide the risk assessment team with the specific risk analysis to be 19

performed. This often includes setting the analytical bounda ries and constraints for the 20

risk analysis. For example, the risk assessment might be focused on risks from exposures 21

to the hazard only within the U

. S

. borders or, perhaps to a particular subpopulation at risk 22

of illness from exposures to the hazard. Th e initial problem formulation and discussion 23

about boundary conditioning are often accomplished interactively with risk assessors who 24

have the particular knowledge about what can be performed quantitatively and whether or 25

not a quantitative risk assessment can be accomplished within the project time 26

constraints. 27

28

Risk managers should work interactively with you ( risk assessors ) during the 29

planning and scoping activit ies to collaborate on defining clear, scientifically defensible 30

―risk question s ‖ before the analytical components of risk assessment are executed ( FDA, 31

2002 ). Forming a risk question is analogous to stating a testable hypothesis at the outset 32

of a basic research project: it is a necessary antecedent to designing an objective and 33

informative proj ect. Here, risk managers are generally aware of the type of information 34

needed to answer policy questions, the resources available to mount complex and large

35

scale risk assessments, and relevant stakeholder concerns. You will probably rely on risk 36

manage rs for a ―big picture‖ perspective of the agency‘s entire portfolio of risk 37

management activities and how the current risk assessment fits into the agency‘s work 38

plan. 39

40

R isk managers have valuable insights into the value and potential problems of risk 41

asse ssments

. Thus, high quality risk management requires risk managers to interfac e 42

with you at various stages throughout the entire risk assessment process so that they can 43

help you anticipate problems in the analyses and redirect resources, if necessary, to 44

improve or ensure the quality of information result ing from the risk assessment. 45

Additionally, risk managers might become aware of new information about the risk in 46Microbia l Risk Assessment Guideline Page 151

Draft

– Do not cite, circulate, or quote question that might be useful for focusing the risk assessment on a modified risk 1

questio n. The frequency of risk assessor and risk manager discussions will likely depend 2

on the complexity and nature of the risk being investigated. 3

4

The risk managers should explain clearly why the assessment is being performed 5

and what questions need to be ad dressed. The risk managers should also advise the 6

assessors, economists, engineers, and other contributing experts involved in the planning 7

and scoping of any interested party, affected party, or policy interests to be considered in 8

the context of the ris k issue. These factors may influence the risk management options, 9

management goals, key participants, data sources, selection of assessment endpoints, or 10

the schedule for the development of the assessment. The risk manager and appropriate 11

others should d iscuss any regulatory basis for the risk assessment and what kind of 12

information is required to satisfy such requirements. 13

14

Risk assessment teams usually have a lead risk assessor. 42 The lead risk assessor 15

is responsible for ensuring that risk assessments a re properly performed and documented 16

and that the key information from risk characterization is elevated up the management 17

chain and communicated to senior management. The lead risk assessor should ensure 18

that the risk characterization integrates other co nsiderations specified in applicable 19

statutes, Agency and office policies, executive orders, and other factors to make and 20

justify regulatory decisions. The lead risk assessor‘s specific responsibilities might 21

include: 22

23

a) Ensure that all risk assessment wor k products produced by or submitted to your 24

organization are well written and characterized. 25

26

b) Provide advice, guidance, and support for the preparation, conduct, and 27

completion of an appropriate risk assessment for your decision. 28

29

c) Play a major role in mana ging and documenting the planning and scoping 30

process. 31

32

d) Ensure that sufficient funds are designated in the office‘s budget request to 33

conduct a risk assessment. 34

35

e) Establish a realistic risk assessment schedule. 36

37

f) Ensure that the products prepared by individu al risk assessors for their portion of 38

each risk assessment document are integrated into a complete risk assessment. 39

40

g) Establish systems to maintain records of the risk assessments prepared by risk 41

assessors under your supervision. 42

43

42 Synonyms for this position include, technical integrator, risk assessment team leader, risk assessment

team liaison, and risk assessment manager.Page 152 Microbia l Risk Assessment Guideline

h) Ensure that the key poin ts from the risk assessment are carried forward in all 1

deliberations or considerations for decision making. 2

3

i) Review implemented decisions for the degree of implementation, efficacy, and 4

ongoing relevance. 5

6

7.3 How are Risk Management Options a Useful Compo nent to Include in a Risk 7

Assessment? 8

9

―Risk managers use information from risk assessment and economic analysis, 10

together with information about pu blic values and statutory requirements, to make 11

decisions about the need for and methods of risk reduction‖ (P/C C , 199 7 ). To 12

accomplish risk management decision

- making, a decision among options for risk 13

management contro ls requires that the decision alter natives for risk management be 14

specified. The characterization of options often means that risk assessors are asked to 15

calculate risks given one or more proposed risk management scenario. Risk managers 16

use these scenarios, the inputs of benefit

- cost asse ssments and other information to make 17

decisions about the best option for controlling risks. 18

19

One of the principles of risk management is that the risk management analysis 20

and the proposed risk control strategy should be commensurate with the level of risk

. 21

The reality of this principle in practice is that it also often relates to uncertainty about the 22

magnitude of risk. Highly uncertain risk estimates sometimes lead risk managers to 23

expend additional risk assessment resources in an effort to reduce the u ncertainties before 24

decisions about controlling the risks are taken. 25

26

From the risk manager‘s perspective, risk assessment is only one among several 27

tools that can be used to inform the risk decision made by decision makers. Decision 28

making by risk manag ers can call for benefits

- risk assessm ents, risk

- risk analysis, VOI 29

analysis

, or trade

- off analysis as additional information useful to making decisions about 30

managing risks. Decision analysis might be used to create a systematic and transparent 31

decision making process that evaluates the importance of factors ranging from the 32

objectively scientific to social values. For most scientific endeavors, ―risk

- informed‖ or 33

―risk

- based‖ decision making benefits from a formal decision analysis that provides a 34

syste matic analysis of complex scientific information, concerns of stakeholders, the 35

constraints on risk management options caused by gaps in data, models or policies, and 36

need for transparency in governmental decision making. 37

38

7.4 What are Some Other Inputs in to Risk Management Decisions About 39

Controlling or Accepting Risks? 40

41

Decision making about risks often requires balancing results of a ri sk assessment 42

with the results of benefit

- risk and risk

- risk tradeoff analyses; the need for risk 43

management resources to address other risks in the agency‘s portfolio; and political 44

pressures from stakeholders in industry, the public

, or legislatures. Th e most important 45

input from risk assessment into the decision

- making is a high

- quality risk 46Microbia l Risk Assessment Guideline Page 153

Draft

– Do not cite, circulate, or quote characterization from which risk managers can evaluate the scientific underpinnings of 1

risk estimates, including a characterization of uncertainties in the estimate s. 2

3

Risk managers make decisions under uncertainty. The results of benefit

- risk and 4

other analyses prior to risk management decision making often include equivalently 5

uncertain estimates for the impact of proposed risk controls on the reduction or 6

elimi nation of risks. Here, risk managers can employ formal decision analysis to trade

7

off decision alternatives, based on both objective and subjective assessments, for a 8

transparent decision. In other situations, the decision to control or accept risks migh t be 9

well

- defined by existing regulation or guidance. Ultimately, the goal of risk management 10

is to achieve an appropriate level of risk. The affirmation of this goal might not occur 11

until after risk management controls are applied and the risks have bee n evaluated 12

iteratively. 13

14

One of the most difficult aspects of risk management for regulatory functions is 15

setting an acceptable or tolerable level of risk. Different approaches to setting standards 16

have different ways of incorporating tolerable risk le vels. You should be aware of what 17

approach has historical precedence in the field that applies to your risk assessment. A 18

summary of some approaches to acceptability that have been suggested to regulators is 19

provided below (adapted from Humber and Almede r 1986, Fischhoff et al. 1981, Lave 20

and Romer 1981, Lowrance 1976): 21

22

a) Reasonableness

– This is a commonly cited principle in safety judgments. For 23

example the Consumer Product Safety Commission is mandated to ―reduce 24

unreasonable risk of injury.‖ 25

b) Custom of usage

– The U.S. Food and Drug Administration (FDA) ―generally 26

recognized as safe‖ (GRAS) determination is for food substances which do not 27

have to be regulated as additives because among other things they have a history 28

of usage. Table salt and sugar are examples. 29

c) Prevailing professional practice or professional judgment

– Originally 30

established for physician‘s clinical practice, the principle is also used for local 31

building standards and toy design. The underlying assumption is that sanction by 32

custom i s safer than untested. In professional judgment professionals rely on 33

personal experience, accepted professional practice and their clients‘ desires to 34

judge risks. 35

d) Best available practice, highest practicable protection, and lowest practicable 36

exposure, best available technology

– air and water quality regulations are 37

associated with these standards, but they still require judgment. Lowest practical 38

is also known as ―as low as reasonably practical‖ (Vatn 2004 ). 39

e) Risk benefit (degree of necessity or benefit )

– A rough balancing of risks and 40

benefits is attempted. 41

f) The Delaney Clause or No

- risk

– named after former New York Congressman 42

James Delaney who added language to the Federal Food

, Drug

, and Cosmetic Act 43

that states ―no [food] additive shall be deemed t o be safe if it is found...to induce 44Page 154 Microbia l Risk Assessment Guideline

cancer in man or animal.‖ It has been criticized for omitting other health 1

endpoints and because it ignores dose

- response relationships. It attempts to lower 2

risks to zero. 3

g) No observable adverse effect level (NOAEL)

– b ecause no adverse effects are 4

observed at a given level of chemical, that level is deemed acceptable. It is 5

customary in chemical risk that when the NOAEL is based on animal data, a 6

safety factor of 10 0 is applied to arrive at the level for humans (decima l point is 7

moved to the left to make the appropriate level 10 0 times lower). 8

h) Cost

- effectiveness

– Equates the cost of saving lives or preventing adverse 9

effects across programs. 10

i) Formal benefit

- cost analysis (also referred to as cost

- benefit analysis)

– 11

qua ntitative framework that includes explicit dollar value of a human life or 12

human wellbeing . Performance of cost

- benefit analysis still only serves to inform 13

the risk management decision. It cannot determine the decision (Williams and 14

Thompson 2004). 15

j) Risk

- risk

– balances various risks against each other. 16

k) Quantitative risk assessment

– is able to provide a range within the actual risk 17

that the acceptable risk can fall. Alternative management assumptions can be 18

tested. 43 19

20

For chemical risk assessment usually the risk level is stated quantitatively. For 21

microbial risk level, regulators very often refer to quantified risk reduction, without 22

actually stating the level of risk associated with the risk reductions or commenting on the 23

acceptability of the level of risk. Some levels of risk that are customary for microbial 24

hazards in different media include: 25

26

a) Ambient recreational water

– The U.S. and the most stringent European Union 27

and WHO standards are associated with about one to two percent (1

- 2%) 28

increased ri sk of gastrointestinal illness due to exposure to ambient water during 29

recreational activities (EPA, 2004 c )

. 30

31

b) Foods

– Hazard Analysis Critical Control Point ( HACCP ) plans are common and 32

are not linked to acceptable health risk levels , but are designed to ensure 33

contamination of food is prevented or otherwise mitigated

. Food Safety 34

Objectives and Performance Objectives are stated goals (often numeric in nature) 35

for public health, processing, transportation, or retail safety (Crouch et al., 2009; 36

Havelaar, et al., 2004; Rieu et al., 2007) . Management options are often linked to 37

quantified public health outcomes. 38

39

c) Biosolids

– Microbial standards are currently based on operational standards 40

which should provide pathogen levels that are below detection limits . The 41

standards are not linked to level of health risk

. 42

43 Proposed control measures must really reduce risks, not transfer them somewhere else (de Koning 1987).Microbia l Risk Assessment Guideline Page 155

Draft

– Do not cite, circulate, or quote 1

d) Ai r

– There are no standards or Threshold Limit Values for microbial pollutants 2

in the U nited States , but numeric criteria for mold and bacteria levels have been 3

set in other countries. Indoor leve ls are compared to outdoor levels. 4

5

e) Vaccinations

– Historically accepted or rejected risk levels of different adverse 6

health outcomes are compared to benefit:risk analysis for new vaccinations (FDA

, 7

1999)

. 8

9

f) Occupational

– The General Duty Clause provides e mployees with workplaces 10

that are ―free from recognized hazards that are causing or are likely to cause death 11

or serious physical harm.‖ Target risk levels for quantified health outcomes are 12

not discussed (29 CFR 1910.1030). 44 13

14

7.5 What are Some Operationa l Risk Management Tools and Approaches? 15

16

A major reason for the development of the public health field was for the 17

prevention of microbial diseases in human population caused by unsanitary conditions 18

(e.g., John S now and the 1854 Cholera outbreak ) or the presence of vectors for disease 19

transmission. The regulatory tools at the disposal of the public health risk manager span 20

an entire range of options to prevent the occu rrence of pathologic organisms (e.g., low

21

acid canning regulations) to limiting the means of primary or secondary transmission to 22

vaccination of the host (e.g., for Bacillus anthrac is) to improve host resistance to disease. 23

Sanitary engineering designs th at provide clean drinking water and separate waste water 24

channels are credited with preventing countless outbreaks of disease (e.g., cholera, 25

typhoid fever) and saving countless lives in more modern times. Even the education and 26

outreach programs to promo te safe handling of raw foods during preparation are part of 27

risk management strategies to reduce the incidence of foodborne illness. 28

29

Generally speaking, operational risk management controls for health hazards are 30

classified as physical, administrative and management controls (Table 7. 2). For the 31

particular case of microbial hazards, a ―biological‖ classification for the host could be 32

added for the possibility of immunization against some of the microbial hazards. 33

34

44 http://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table=STANDARDS&p_id=10051Page 156 Microbia l Risk Assessment Guideline

Table 7. 2 Cla ss of Risk Management Controls for Operational

- Level Microbi al 1

Risk 2

Class of Operational

Risk Controls Examples

Physical Barriers and physical factors that eliminate or reduce

the chance of contamination and growth of pathogens

Pasteurization with heat or ionizing radiation

Packaging

Disposable gloves

Administrative Engineering designs: process controls

Work practices and standards

Sanitary practices by personnel

Training program design

Managerial Training execution

Supervision

Appropriate skills for th e tasks that might lead to

contamination or favorable growth

Biological (specific

microbes) Vaccin ation

Food animal treatments with benign organisms

3

7.6 What is Risk Management for the Intentional U se of R egulated 4

Microorganisms? 5

6

Microorganisms, whose intentional uses fall under federal laws, including, but not 7

limited to, the Federal Insecticide, Fungicide, and Rodenticide Act, the Federal Food, 8

Drug, and Cosmetic Act, the Plant Protection Act, the Endangered Species Act, and the 9

Invasive Species Act, often have specific risk management options available to the 10

regulators. These options can involve placing restrictions on their use or, if justified, 11

de nial of permission for their use. Restrictions on their use can vary according to the 12

specific law and regulations involved. For example, for pesticides, the restrictions can be 13

used to limit exposure by specifying on the approved labels exactly when, wh ere, and 14

how much can be applied. Furthermore, protective clothing, including respiratory masks, 15

may be required to be used. Restrictions can also be set on what levels are acceptable to 16

appear on food crops, although due to the potential for growth subs equent to application, 17

microorganisms with any potential for human toxicity and/or pathogenicity are generally 18

not allowed to be used on food crops. Containment requirements can be placed on 19

intentional uses of regulated microorganisms for field testing a nd for industrial uses. 20

Genetic engineering can be used to reduce risk concerns for intentionally used and 21

regulated microorganisms, e.g. by deleting toxin genes. In addition, marker genes can be 22

used to better identify the specific approved uses. Restr ictions on the storage and 23

movement of the regulated microorganisms can also be used to mitigate risk. 24

25

26Microbia l Risk Assessment Guideline Page 157

Draft

– Do not cite, circulate, or quote 

## 8. RISK C OMMUNICATION 1

2

A risk assessment is only as good as your communication of its output . All of the 3

t ime and effo rt you spend conducting a risk assessment is for naught unless there is clear 4

communication of risk assessment results to risk managers and other stakeholders. 5

Effective risk communication requires diligence and has the same level of importanc e as 6

any other component of the assessment. 7

8

This G uideline does not provide a detailed treatment or guidance for risk 9

communication; you can refer to other places for such detail (Sellnow, 2008; Morgan, 10

2002; Lundgren and McMakin, 1998) . Rather, this cha pter provides an initial 11

understanding of how risk communication plays an important role in the risk assessment 12

process. 13

14

Risk assessors are not expected to carry the load o f risk communication. You 15

need to work with the appropriate communication offices in your Agency (e.g., public 16

affairs office , Congressional outreach office ). These communicators need to be part of 17

the team during planning and scoping and throughout the remainder of the risk 18

assessment process. 19

20

8.1 What is Risk Communication? 21

22

Risk communication describes and exchanges information about risk, including 23

its form and severity, and what can be done to lessen or avoid it. It includes two equally 24

important objectives: 25

26

a) In form risk manag ers about risk so that they may make informed decisions

. 27

28

b) I nform the public about risk so that they understand the nature of the risk and 29

what is being done or will be done about it. 30

31

Risk communication is the interactive exchange of information and opinio ns 32

concerning risks and risk management among risk assessors, risk managers, consumers, 33

and other interested parties (WHO, 2000). 34

35

8.2 What are the Aspects of Risk Communication? 36

37

Risk c ommunication principles include (OMB 2007b): 38

39

a) Risk communication should involve the open, two

- way exchange of information 40

between professionals, including both policy makers and ―experts‖ in relevant 41

disciplines, and the public. 42

b) Risk management goals shoul d be stated clearly, and risk assessments and risk 43

management decisions should be communicated accurately and objectively in a 44

meaningful manner. 45Page 158 Microbia l Risk Assessment Guideline

To maximize public understanding and participation in risk

- related decisions, 1

agencies should: 2

1) explain the bas is for significant assumptions, data, models, and 3

inferences used or relied upon in the assessment or decision; 4

2) describe the sources, extent and magnitude of significant uncertainties 5

associated with the assessment or decision; 6

3) make appropriate risk compar isons, taking into account, for example, 7

public attitudes with respect to voluntary versus involuntary risk; and, 8

4) provide timely, public access to relevant supporting documents and a 9

reasonable opportunity for public comment. 10

11

The aspects of a goo d risk co mmunication plan (see S ectio n 2. 6. 6 ) i nclude: 12

13

a) Involvement and input of risk managers and stakeholders throughout the risk 14

assessment process 15

b) C lear risk management questions that are understood by managers and other 16

assessors 17

c) A wareness by managers and othe r stakeholders of the strengths and limitations of 18

the assessment 19

20

Social and personal behaviors are strongly influenced by risk managers‘ 21

pronouncements

, but only if the underlying risk assessment process is transparent to the 22

public and considered credib le . Trust is based on open communication and the credibility 23

of provided information ; thus, the public must be aware of the sci ence behind the risk 24

assessment

. People who write r isk communication statements should consider 25

acknowledging both the power an d the limits of the risk assessment process and the data 26

used in the risk assessment

. 27

28

8.3 Who are the Stakeholders of MRAs? 29

30

Stakeholders are people or organizations that may be affected by the re levant 31

decision and thus, have an in t erest in the outcome of the risk assessment . They typically 32

include people such as those in industry who will be responsible for implementing and 33

financially affected by new rules and regulations borne of the risk asse ssment, as well as 34

the general public. For example , a risk assessment that results in new regulations for 35

Escherichia coli in ground beef would affect all persons who produce and consume 36

ground beef. Anyone interested in a risk assessment may be reasonab ly termed a 37

stakeholder. S takeholder groups will self

- identify but should also be sought out

. 38

39

Risk managers, assessors, and communicators are not typically considered 40

stakeholders because they should remain unbiased . If the results of the risk assessment 41

could affect them and the outcome was altered because of this knowledge, this would be 42

considered a conflict of interest. 43Microbia l Risk Assessment Guideline Page 159

Draft

– Do not cite, circulate, or quote 1

8.4 With Whom Can I Communicate? 2

3

Even if risk communication specialists are o n the team, y ou as the risk assessor 4

may serve in a risk communicator role and be responsible for communicat ing with 5

stakeholders and develop ing outreach materials

. If this is the case , you can work closely 6

with appropriate Agency offices to follow protoc ol. For example, many agencies will 7

request that communication with members of Congress be done t hrough a specific office. 8

C ommunication strategies and materials for different stakeholders should be tailored to 9

that particular audience. For example, com munication with technical experts c ould have 10

a high level of technical detail , while communication to the lay public would be less 11

technical. 12

13

The language used in communication materials should conform to your Agency‘s 14

standards, but needs to be absolut ely comprehensible by almost all being addressed. 15

Agency communications, public affairs, and/or outreach offices should be consulted to 16

assure that the message can be understood. Your agency may also have specific guidance 17

for coordinating with other fed eral, state, and local health and environmental agencies. 18

19

8.5 When Can the Process of Risk Communication Begin? 20

21

There is a tendency to view risk communication as a final stage to t he risk 22

analysis process

— something that occurs upon completion of the risk assessment and risk 23

management. It is critically important that risk communication strategies be developed at 24

the beginning of a risk assessment (during planning and scoping), to i nform risk 25

managers and other stakeholders throughout the risk assessment. The best place to start 26

the risk communication process is during planning and scoping discussions in which a 27

risk communication specialist can draft a risk communication plan (i.e.

, the 28

communication strategy , including the risk assessor ‘ s role ). 29

30

During development of the risk assessment, the team leader can be proactive in 31

implementing the risk communication plan, particularly communicating with risk 32

managers and stakeholders. Fo r example, one way to do this is by announcing that the 33

Agency plans to conduct a risk assessment. An announcement can be placed in the 34

Federal Register . Other good venues for announcing risk assessments (and subsequent 35

activities) include the agency‘s w eb site, advertising/announcement sections of trade and 36

professional journals, and at professional meetings. 37

38

8.6 Can I Communicate in Writing

, Orally, or Both? 39

40

Both. Oral communicati on is needed at many points throughout development of 41

the risk assessment. It is appropriate to keep risk managers and other stakeholders 42

informed about progress of the risk assessment. For example, you may wish to schedule 43

a weekly, (or other appropriat e frequency) phone call with managers to keep them 44

updated. Similarly, periodic conference calls or public meetings with stakeholders lessen 45

the chance that they are caught off guard when the risk assessment is completed. 46Page 160 Microbia l Risk Assessment Guideline

1

Communication in writing is almo st always appropriate

, especially since a written 2

record is usually needed . For example, it is good to have the risk assessment questions in 3

writing. Doing so entails back

- and

- forth work with risk managers to identify and clearly 4

articulate the purposes of the risk assessment. By solidifying the questions in writing, 5

you help ensure that risk managers, risk assessors, and stakeholders clearly understand 6

what the risk assessment is intended to do. 7

8

The most current risk communication message should be av ailable in some 9

written form (e.g., fact sheets), as well as electronically, throughout the process. 10

Information should be available on your Agency‘s website (although it should also be 11

accessible to all who do not have computer access). Coordination wit h local health 12

authorities in most cases is also important, and they may be able to suggest useful 13

approaches to communicating with their public. 14

15

Virtually all completed risk assessments include a written report. The TCCR 16

principles discussed and integra ted throughout this Guideline will help with the effective 17

written communication in the form of the report (Section 1.10 )

. In addition, many risk 18

assessments are presented publicly as presentations or seminars. 19

20

8.7 Who Decides What to Communicate? 21

22

The risk manager

— in consultation with the risk assessor

— is responsible for 23

deciding what information to communicate. Any formal communication or information 24

release should be firmly based on the docu mented findings of the risk assessor. 25

Therefore, you are responsible for communicating risk to the risk manager for a full 26

understanding of the potential risks that would then be considered in the decision making 27

process. Again, risk communicators can as sist in the delivery and presentation of the 28

information to the public. 29

30

MRA is an inexact science, requiring judgment calls and policy decisions on the 31

part of highly trained, experienced professionals. This message should be included in 32

any communicatio n of risk, following a statement of known facts and preceding 33

prescriptive risk reduction measures. Mathematical constructs and underlying 34

assumptions should be made clear. Although there may be differences of opinion among 35

risk assessors, the risk manag er is ultimately responsible for deciding on a transparent 36

and clear message on which the audience can evaluate agency actions. 37

38

8.8 What Information Can be Communicated? 39

40

The content of the for mal risk assessment (as communicated to the public or to 41

stakeholders) is determined by the risk manager , b ut you are responsible for present ing 42

all available data, including those that challenge or do not support points in the 43

assessment. The information that the risk manager may decide to communicate include s : 44

45Microbia l Risk Assessment Guideline Page 161

Draft

– Do not cite, circulate, or quote a) Data on human disease identified as either historical or experimentally

- derived, or 1

projections generated by modeling 2

b) Underlying uncertainties or data gaps 3

c) The degree of potential hazard to sensi tive populations such as children

, the 4

elderly , and immune compromised people (focusing o n susceptibility and 5

severity) 6

d) Explanation that the d ifferent pathways that were deemed relevant were explored 7

thoroughly 8

e) The possibility of person

- to

- person transmiss ion (if applicable) 9

f) Any results from animal testing and how these data may be relevant to humans 10

g) The potential for zoonotic transmission between humans and animals (if 11

applicable) 12

h) P otential actions to reduce exposures ( for example, following posted signs 13

r egarding swimming, fishing, or harvesting clams ) 14

15

You will have to provide the risk manager with your best professional judgment 16

at that time as to the degree of risk associated with a specific hazard. This judgment can 17

be accompanied by all rele v ant data about both the hazard and the target population. 18

Probably some of the most important pieces of information you need to communicate are 19

description s of the probabilities, uncertainties, and possible sources of biases or error 20

(assumptions, generalizations ) in any interim or final assessment. 21

22

8.9 How i s the Communication Process a Continuous Dialog ? 23

24

Effe ctive risk communication is a continuous process that requires constant 25

feed back throughout the risk assessment . This begins at the planning and scoping stage, 26

when all parties involved ( including stakeholders as appropriate ) should have a chance to 27

comment on the need for a risk assessment, the scope of the risk assessment , and the 28

other factors discussed in planning and scoping . Doing this helps to promote buy

- in and 29

helps to ensure respect for the process. C ommunication of risk from assessors to 30

managers is constant throughout the process . Communication of risk from risk man agers 31

to the public is usually episodic, but nonetheless, scheduled regularly. 32

33

As an example, interactive communication occurs between risk managers and risk 34

assessors in developing risk management questions. A risk manager may say s/ he wants 35

a risk asse ssment to address ― Salmonella .‖ It is then up to you to press for more 36

specifics. At this point, perhaps the manager refines the question to ―What is the effect of 37

increased cooking temperatures on illnesses from Salmonella ?‖ Then you may indicate 38

that data are only available for a specific serotype, Enteritidis for example, but not all 39

serotypes. Thus, the manager can work to refine the question to include Enteritidis only. 40

In the end, iterative communication helps ensure clear and concise questions, which in 41

turn increases the likelihood of a useful risk assessment. This iterative dialog can be 42

conducted in person to speed up the process, but written documentation of the 43Page 162 Microbia l Risk Assessment Guideline

understandings between risk managers and risk assessors is important for clarity and the 1

orientation of new team members. 2

3

Communication can also be iterative when describing results of the assessment to 4

managers and other stakeholders. In virtually all cases , there is considerable room for 5

improvement in risk assessments. Therefore , it is unwise to present results from risk 6

assessments as if they were written in stone. Instead, the results should be presented as a 7

best effort, with the idea that feedback from managers and stakeholders will likely 8

improve the assessment. 9

10

8.10 How I n

- Depth Can I Communicate? 11

12

Depth of communication depends on the audience. For example , if you are 13

describing the risk assessment to another risk assessor, perhaps one who will peer review 14

the asse ssment, then your communication should be very detailed . If, on the other hand, 15

you are presenting the results to a high

- level risk manager in the space of ten minutes, in

16

depth details should be avoided unless specifically sought. When communicating wit h 17

stakeholders, it is important to present results in a clear manner without talking down to 18

the audience. For example, do not say, ― T his next part is complicated, so I‘ll put it in 19

terms you can understand.‖ Instead, start with a broad description of th e work and then 20

proceed to the details as both time and the audience‘s needs dictate. Lastly, it is very 21

important to write a detailed report of the assessment. That way, even though you may 22

be unable to give specifics during a talk, briefing, or other v enue, you will always be able 23

to refer your audience to the written report. The detailed report should be sufficiently 24

detailed that another team of risk assessors with appropriate expertise can replicate the 25

risk assessment. In addition to a formal deta iled report, summary graphics, executive 26

summary, fact sheets , and other types of overview documents can aid in communication 27

efforts. 28

29

8.11 What Can I Do if the Message I s Not “Getting Through?” 30

31

It is incumbent upon you as a risk assessor to convey the results of the 32

assessment. If you cannot do this, then the utility of the assessment is lessened. 33

Accordingly, when you communicate the results of the assessment (or any asp ect of the 34

assessment for that matter), work to engage your audience. Take time to explore if your 35

audience understands the points you are trying to communicate. If not, take a step back 36

and work to clarify the parts of your message that are confusing. Look at this as an 37

opportunity to exchange information and thoughts with your audience. K eep in mind that 38

the difficulty may l ie in your communication, not in your audience‘s comprehension. It 39

may help if you communicate through various means. For examp le, in addition to 40

speaking, it may help to take out pencil and pad and sketch your message. Be sure to 41

allow time throughout your communication (be it a formal slide show or an informal 42

conversation) for the audience to seek clarification. When experien cing problems getting 43

through to the audience , consult communication specialists within your Agency that can 44

assist with communication. 45

46Microbia l Risk Assessment Guideline Page 163

Draft

– Do not cite, circulate, or quote 8.12 How Can I Communicate Risk Successfully? 1

2

Succe ssful risk communication requires strategic planning. This planning 3

requires in turn a very thorough review of the costs and benefits of specific actions (or 4

inaction), and considers possible outcomes. Successful risk communications consider the 5

public t o be stakeholders, inasmuch as Agency decisions and actions affect them directly. 6

The stakeholders base their own behaviors on information (however anecdotal) provided 7

by trusted sources. Strategic risk communication practices may help to develop audienc e 8

understanding and ultimately gain stakeholder and public cooperation. 9

10

Strategic risk communication involves planning how to address stakeholder 11

questions identified during planning and scoping and later during the assessment process. 12

The responses to these questions should be straightforward and couched in simple 13

language (rather than technical jargon). Concepts need to be packaged correctly, i.e., 14

clearly, truthfully, and respectfully. This last requirement

— that relevant concepts be 15

presented in und erstandable ways that enable discussion amongst all stakeholders , rather 16

than as abstruse knowledge suitable for expert analysis only

— is critical in establishing 17

successful communication. 18

19

Risk managers turn to strategic risk communication when they need ― buy

- in.‖ 20

The best way to achieve cooperation of public and stakeholders in, rather than agitation 21

against , risk

- management decisions is by helping the audiences choose among options 22

that involve cost (risks) and safety (benefits). Usually n o one on the t eam can foresee the 23

full range of audience response s . The starting relationship between Agency and audience 24

may be skeptical and/or confrontational, pitting audience experience against expert 25

analysis. Strategic risk communication helps the audience to g ain insight into the 26

problem (and/or proposed actions) and to establish exactly what aspects of the propo sed 27

action (timing? approach?) are within their control. Ideally, successful risk 28

communication will form the basis for mutual trust; but at the very least, strategic risk 29

communication transfer s information. 30

31

People respond not only to what is said, but how it is said . In addition, people 32

response to the way in which actions are carried out . Stakeholders value the qualit ies of 33

listening, understandin g, and responsiveness on the part of the Agency

. Other important 34

factures include t heir own perception of risk acceptability

, due process ( in which 35

stakeholders are able to participate in judging risks and predicted benefits ), their personal 36

sense of the risk

- manager‘s credibility

, and, above all, open communication 37

(transparency). Successful risk communication is developed with full appreciation of the 38

technical complexity of the situation in question, the controversy about or unavailability 39

of the requi site science, the sensitivity of the communication environment, the potential 40

relevance of political realities, and the perceived credibility of parties involved. 41

42

Other preparations can also contribute to successful communication. For example, 43

practicing presentations before audiences such as co

- workers can test the 44

understandability of the presentation and increase the presenter‘s comfort level. The 45

length of presentations is also an important consideration, since typically audiences do 46Page 164 Microbia l Risk Assessment Guideline

not want long pre sentations without opportunities for to ask questions. Furthermore, 1

p ractical information such as what actions can be taken by individuals or communities to 2

interrupt exposures (e.g., beach closures to limit exposure to fecal contamination) can be 3

include d whenever the risk assessment is presented to the public, even if the actions 4

suggested are not directly addressed or considered in the risk assessment. 5

6

8.13 How Can I Handle Media and Congressional Office Requests? 7

8

Risk assessors are sometimes approached directly with questions by external news 9

sources and congressional offices. It is the responsibility of the risk manager and/or your 10

communications offices (e.g., public affairs office, congressional liaison office), not the 11

risk assessor, to communicate risk. If you are contacted directly from someone in the 12

media or a congressional office, inform that person that your communications office is 13

authorized to answer their questions (e.g., requests for interviews, background 14

information, policy questions). Provide them with your communications office contact 15

information (phone number, e

- mail address). If you are contacted directly via e

- mail, 16

simply forward the message to your supervisor and communications office. No matter 17

how you are contacted, inform your supervisor and include as many details about the 18

request as you can. 19

20

However, there will be occasions that

, with appropriate permissions, you can 21

provide technical information relevant to the risk assessment being queried. Remember 22

that there is no such thing as ―off the record.‖ Assume everything said is on the record; 23

even ―background‖ information is still a response. Once you provide information, 24

nothing reall y prevents the recipient from including that information in their article or 25

report. A good reporter for example may not use ―off the record‖ information as directly 26

attributable to you, but will have a lead to contact others to verify it and then may use or 27

release that information. You might also consider having a press officer on the phone or 28

during interview s to make sure that the appropriat e information is communicated. You 29

may need to be aware of press deadlines and might be asked to contribute gra phics to 30

include in article s where appropriate. 31

32

8.14 When Can Risk Communication End? 33

34

Once a decision has been made

, fully implemented

, and openly communicated as 35

per the risk communication plan , subsequent communication efforts may not be as 36

intensive. However, there will likely be a need to monitor how the implemented decision 37

is accomplishing its goal(s) or not, and communication will be critical at that time . You 38

need to be aware that resul tant actions based on the risk assessment may need to be re

39

evaluated and addressed further in the future (and this can be part of your communication 40

plan)

. However, be careful not to mislead the public into expectations of involvement 41

and re

- evaluation t hat might not be appropriate

. 42

43

Risk communication shouldn‘t have an absolute end ; it can be an ongoing 44

process. In another vein, risk managers can take the initiative to incorporate risk 45

communication into routine functions. For example, the various micr obial societies 46Microbia l Risk Assessment Guideline Page 165

Draft

– Do not cite, circulate, or quote (most no tably, in the United S tates , the ASM ) produce informational outreach material 1

targeting specific age groups and educational levels. These professional societies seek to 2

expand public understanding of the role of microorganisms in h uman affairs beyond 3

disease causation through staff dedicated to the effort. Agency public affairs offices 4

might fulfill their responsibility to the public by teaming with these societies and by 5

working with internal environmental

- education staff to devel op guidelines and 6

procedures for ongoing risk communication. At least one senior public affairs manager 7

could be tasked to work with Agency experts to identify and communicate risk specific to 8

children, seniors, and other sensitive populations. Interacti on with public health and 9

safety agencies (the Centers for Disease Control and Prevention [CDC] or the 10

Occupational Safety and Health Administration [OSHA] ) would be advisable, as would 11

consultation with academic and industrial clinicians. A database of t hese and other 12

external consultants can be developed in anticipation of potential outbreaks. 13

14

The risk communication network just described would be expected to develop 15

outreach materials continuously, and to establish name recognition for the responsible 16

Agenc y via routine (e.g., bimonthly ) public education broadcasts or activities. Such 17

learning opportunities could be easily incorporated into local public school curricula, and 18

ideally would be organized and distributed by agency offices nationwide. The network, 19

once in place, can also provide informational materials to concerned individuals (and 20

Internet blogs) or to news outlets. 21

22

23Page 166 Microbia l Risk Assessment Guideline



## 9. GLOSSARY 1

2

The term definitions in this glossary are from the EPA Thesaurus of Terms Us ed in MRA 3

unless otherwise noted (EPA, 2007b). For the original sources of the definition, see the 4

Thesaurus. 5

6

acceptable risk 7

This is a risk management term. The acceptability of the risk depends on scientific data, 8

social, economic, and political facto rs, and on the perceived benefits arising from 9

exposure to an agent. Tolerable risk is a synonym. 10

11

analysis of variance 12

This is a statistical technique that isolates and assesses the contribution of categorical 13

factors to variation in the mean of a contin uous outcome variable. The data are divided 14

into categories based on their values for each of the independent variables, and the 15

differences between the mean outcome values of these categories are tested for statistical 16

significance. 17

18

analysis plan 19

This is a plan that provides all the details of exactly how each part of the risk assessment 20

will be performed. It usually describes in detail what analyses will be performed, how 21

they will be performed, who will perform the work, schedules, resources, qualit y 22

assurance/quality control requirements, and documentation requirements. 23

24

appropriate level of protection (ALOP) 25

Codex defines ALOP as the l evel of protection deemed appropriate by the member 26

(country) establishing a sanitary or phytosanitary measure to protect human, animal, or 27

plant life or health within its territory. The term is also used more broadly to refer to risk 28

levels selected for regulations, rules, and risk assessments. 29

30

CODEX 31

The Codex Alimentarius Commission was created in 1963 by FAO and WHO to develop 32

food standards, guidelines and related texts such as codes of practice under the Joint 33

FAO/WHO Food Standards Programme. The main purposes of this Programme are 34

protecting health of the consumers and ensuring fair trade practices in the foo d trade, and 35

promoting coordination of all food standards work undertaken by international 36

governmental and non

- governmental organizations

. 45 37

38

conceptual model 39

Ecological risk assessment defines a conceptual model as a written description and/or a 40

visual re presentation of actual or predicted relationships between humans or ecological 41

entities and the chemicals or other stressors to which they may be exposed. ILSI (2000) 42

states that a conceptual model depicts the purpose, defines the scope and scale, 43

45 http://www.codexalimentarius.net/web/index_en.jspMicrobia l Risk Assessment Guideline Page 167

Draft

– Do not cite, circulate, or quote determi nes appropriate variables and identifies data needed for risk assessment. It can 1

also serve as a preliminary or exploratory risk assessment. 2

3

cost

- benefit analysis and c ost

- effectiveness analysis (CEA) 4

See OMB (2003) for full descriptions of cost

- benefi t analysis and CEA. 5

6

data objectivity 7

Data objectivity ― focuses on whether the disseminated information is being presented in 8

an accurate, clear, complete, and unbiased manner, and as a matter of substance, is 9

accurate, reliable, and unbiased. ‖ (OMB, 2002) 46 10

11

dose

- response 12

This is a relationship in which a change in amount, intensity, or duration of exposure to a 13

pathogen is associated with a change in the manifestation and magnitude of human health 14

effects

. 15

16

dose

- response assessment 17

This is t he determinati on of the relationship between the magnitude of exposure (dose) to 18

a chemical, biological or physical agent, and the severity and/or frequency of associated 19

adverse health effects (response). 20

21

dose

- response curve 22

This is a graphical representation of the quantitative relationship between administered, 23

applied, or internal dose of a chemical or agent, and a specific biological response to that 24

chemical or agent. 25

26

dynamic model 27

This c onsiders the individual within a community rather than the isolated indiv idual. 28

Time

- dependent elements such as secondary transmission, host immunity, and animal 29

reservoirs are included. 30

31

endpoint 32

For chemical risk assessment an endpoint is a n observable or measurable biological event 33

or chemical concentration (e.g., metaboli te concentration in a target tissue) used as an 34

index of an effect of a chemical exposure. For ecological assessment an endpoint is an 35

explicit expression of the environmental value that is to be protected, operationally 36

defined by an ecological entity an d its attributes. For example, salmon are valued 37

ecological entities; reproduction and age class structure are some of their important 38

attributes. Together ―salmon reproduction and age class structure‖ form an assessment 39

endpoint. For MRA an endpoint is usually a health effect or infected state. However 40

indicators or conditions associated with human health could also be endpoint s

. 41

42

43

46 http://www.whitehouse.gov/omb/fedreg_reproducible/Page 168 Microbia l Risk Assessment Guideline

epidemiology triad 1

T his is t he traditional model of infectious disease causation. I t i ncludes three 2

components: an extern al agent, a susceptible host, and an environment that brings the 3

host and agent together, so that disease occurs. 4

5

exposure 6

Exposure is c ontact made between a chemical, physical, or biological agent and the outer 7

boundary of an organism. Exposure compri ses the sources, mode, route, and extent of 8

contact with the microbial hazard(s) of concern. How often a person is exposed is 9

referred to as frequency of exposure. How long a person is exposed to a microbial hazard 10

is referred to as the duration of expos ure

. 11

12

exposure assessmen t 13

T his is t he process of estimating or measuring the magnitude, frequency, and duration of 14

exposure to a microbial hazard(s), along with the number and characteristics of the 15

person or population exposed . The route of exposure is also considered. 16

17

exposure pathwa y 18

The exposure pathway is the physical and temporal movement of microorganisms from 19

their source to the occurrence of an exposure . For chemicals the exposure pathway is t he 20

route a substance takes from its source (where it began) to its end point (where it ends), 21

and how people can come into contact with (or get exposed to) it. An exposure pathway 22

has five parts: a source of contamination (such as an abandoned business); an 23

environmental media and transport mechanism (such as movement through 24

groundwater); a point of exposure (such as a private well); a route of exposure (eating, 25

drinking, breathing, or touching), and a receptor population (people potentially or 26

actually exposed). When all five parts are present, the expos ure pathway is characterized 27

as ― complete ‖, that is, capable of contributing to human health risks. 28

29

frank pathogen 30

A microorganism capable of producing disease in both healthy and compromised 31

persons. 32

33

hazard 34

The term hazard can be interpreted in a numb er of ways. It may be defined as the 35

stressor agent capable of causing an adverse effect on the recipient or the adverse effect 36

itself. The selection of the definition is a policy decision driven by the existing statutes, 37

regulations, or consistency with in

- house processes. Codex considers a microbiological 38

hazard is a hazard arising from bacteria, viruses, yeasts, molds and algae, parasitic 39

protozoa and helminthes , and their toxins or metabolites. 40

41

hazard identification 42

This is the process of determini ng if data support the case for a chemical or a microbe 43

causing adverse health effects in humans and what those effects might be. 44

45

46Microbia l Risk Assessment Guideline Page 169

Draft

– Do not cite, circulate, or quote health effect 1

This is t he clinical manifestation of disease associated with a specific pathogen, including 2

symptomatic and asymptomatic infections, clinical illness, mortality, and sequelae

. 3

4

health endpoint 5

This is a n observable or measurable biological event used as an index to determine when 6

a deviation in the normal function of the human body occurs. 7

8

host 9

This is a p erson or other living animal, including birds and arthropods

, that affords 10

subsistence or lodgment to an infectious agent under natural conditions. In an 11

epidemiologic context, the host may be a population or a group. 12

13

host specificity 14

This is t he chara cteristic of a pathogen that renders it capable of infecting one or more 15

specific hosts. 16

17

immunocompromised 18

Immunocompromised i ndividuals have a weakened immune system, making them more 19

susceptible to infections than the general population

. 20

21

incubation p eriod 22

This is t he time from the moment of inoculation (exposure) to the development of the 23

clinical manifestations of a particular infectious disease. 24

25

i ndicator 26

An indicator is any biological entity or processes, or community whose characteristics 27

show the presence of sp ecific environmental conditions. 28

29

infectious dose 30

This is t he number of organisms that make individuals ill or carriers. It should be noted 31

that methods to count microbes may not be counting individual microorganisms. For 32

example a colo ny forming unit (cfu) may be a clump of cells that formed one colony on a 33

plate. An infectious dose is the minimum number of organisms that will result in entry 34

through the host barriers, survival of the pathogen

, and multiplication in the host. 35

Infectio n may or may not result in symp t omatic illness. On a population basis, there is no 36

discernible minimum infectious dose for pathogens (FAO/WHO 2003)

. I nstead there is a 37

probability distribution for infection associated with different dose levels reflectin g intra

38

and inter

- individual variability in the pathogen

- host relationship

. 39

40

infectivity 41

Infectivity describes the ability of a pathogen to enter, survive and multiply (infect) a 42

host

. 43

44

45Page 170 Microbia l Risk Assessment Guideline

microorganism 1

These are viruses, b acteria, yeasts and simple fungi, single

- celled algae, protozoa, a ll are 2

defined as being organi s ms that can only be seen with the aid of a microscop e . Most are 3

beneficial but some produce disease (pathogens)

. Non

- pathogenic microorganisms are 4

critical for recycling energy and nutrients globally, such as in soil, the oceans

, 5

composting and sewage secondary treatment. 6

7

p athoge n 8

These are m icroorganisms (e.g., viruses, bacteria, protozoa and the ova of helminth 9

parasites) that can cause disease in humans, animals and plants. 10

11

pathogenicit y 12

Pathogenicity refers to the ability of an organism to cause disease (i

. e

. , harm the host). 13

This ability represents a genetic component of the pathogen and the overt damage done to 14

the host is a property of the host

- pathogen interactions. Commensals and opportunistic 15

pathogens lack this inherent ability to cause disease. However, disease is not an 16

inevitable outcome of the host

- pathogen interaction and, furthermore, pathogens can 17

express a wide range of virulence. Virulence, a term often used interchan geably with 18

pathogenicity, refers to the degree of pathology caused by the organism. The extent of 19

the virulence is usually correlated with the ability of the pathogen to multiply within the 20

host and may be affected by other factors (

. i

. e, conditional). In summary, an organism 21

(species or strain) is defined as being pathogenic (or not), and depending upon 22

conditions, may exhibit different levels of virulence. 23

24

P athogenicity is the quality or state of being pathogenic, the potential ability to produce 25

dise ase. Virulence is the disease producing power of an organism, the degree of 26

pathogenicity within a group or species

. 47 27

28

planning and scoping 29

This is the process that defines the purpose and scope of a risk assessment and focuses 30

the issues and approach(es) involved in performing the assessment. 31

32

problem formulation 33

I n ecological risk assessment, problem formulation is the initial stage of a risk assessment 34

where the purpose of the assessment is articulated, assessment endpoints and a 35

conceptual model are de veloped, and a plan for analyzing and characterizing risk is 36

determined. 37

In microbial assessment, problem formulation is a systematic planning step that identifies 38

the goals, breadth, and focus of the MRA , the regulatory and policy context of the 39

assessme nt, and the major factors that will need to be addressed for the assessment. 40

41

qualitative risk assessment 42

Qualitative risk assessment use s verbal descriptors of risk and severity as well as 43

uncertainty, and often involve s the aggregation of expert opin ions

. The results are often 44

47 For further discussion of these terms visit

http://scienceblogs.com/effectmeasure/2006/06/pathogenicity_virulence_transm.phpMicrobia l Risk Assessment Guideline Page 171

Draft

– Do not cite, circulate, or quote stated in an estimated range, such as ―there is a moderate to high risk of a certain 1

outcome occurring.‖ 2

3

quality

- adjusted life yea r 4

This is a unit of health care outcomes that adjusts gains (or losses) in years of life 5

subseq uent to a health care intervention by the quality of life during those years. QALYs 6

can provide a common unit for comparing cost

- utility across different interventions and 7

health problems. Other units for measuring health outcomes include DALYs and 8

healt hy

- years equivalents (HYEs). 9

10

quantitative risk assessment 11

In quantitative assessments, the risk is expressed as a mathematical statement of the 12

chance of illness or death after exposure to a specific hazard, and it represents the 13

cumulative probabilitie s of certain events happening and the uncertainty associated with 14

those events

. 15

16

risk analysis 17

A process consisting of three components: risk assessment, risk management and risk 18

communication. 19

20

risk assessment 21

In the context of human health , risk assessme nt is a systematic way to prepare and 22

organize information and help establish programs, R&D, and regulatory priorities ; the 23

qualitative or quantitative characterization of the potential health effects of particular 24

substances on individuals or populations ; a scientifically based process consisting of the 25

following steps: (i) hazard identification, (ii) hazard characterization, (iii) exposure 26

assessment, and ( 4) risk characterization ; the formal, scientifically based process to 27

estimate the likelihood (pr obability) of exposure to a hazard and the resulting public 28

h ealth impact from this exposure. The product of the risk assessment is often a statement 29

regarding the probability that populations or individuals so exposed will be harmed and 30

to what degree (r isk characterization). 31

32

risk characterization 33

In risk characterization, risk due to a particular exposure for a d efined population is 34

described in coherent, understandable, and informative conclusion s about the 35

microbiological risk to exposed humans in a way that is useful for decision makers as 36

well as stakeholders in that risk

. In all cases, major issues and uncertainty and variability 37

associated with determining the nature and extent of the risk should be identified and 38

discussed. The risk characteri zation should be prepared in a manner that is clear, 39

transparent, reasonable, and consistent. 40

41

risk communication 42

T his is t he exchange of information about health or environmental risks among risk 43

assessors and managers, the general public, news media, a nd other stakeholders. WHO 44

considers risk communication to be the interactive exchange of information and opinions 45Page 172 Microbia l Risk Assessment Guideline

concerning risks and risk management among risk assessors, risk managers, consumers, 1

and other interested parties (WHO, 2000). 2

3

risk manageme nt 4

In the context of human health, risk management is a decision making process that 5

accounts for political, social, economic and engineering implications together with risk

6

related information in order to develop, analyze and compare management options an d 7

select the appropriate managerial response to a potential chronic health hazard. 8

9

risk profile 10

Developing a risk profile involves an initial systematic collection of information, which 11

is evaluated to determine what other actions (including a MRA ) and resources may be 12

needed . The risk profile is a n overall summary of the context in which a risk is being 13

analyzed, including: a description of the risk(s) considered, values threatened by the risk, 14

social perception of the risk, who benefits from producing the risk, who benefits from 15

managing the risk, and characteristics of the risk, the risk

- producer and the risk

- bearer, 16

which are pertinent to successful management of the risk

. 17

18

secondary transmission 19

This is the d irect or indirect propagation of a pathog en from an infected person (with or 20

without clinical illness) to additional people. 21

22

sensitivity analysis 23

Sensitivity analysis examines the relative influence and importance of a model‘s inputs 24

on its output measuring the  ̳relative‘ influence . It is the p rocess of changing one variable 25

while leaving the others constant to determine its effect on the output. This procedure 26

fixes each uncertain quantity at its credible lower and upper bounds (holding all others at 27

their nominal values, such as medians) and computes the results of each combination of 28

values. The results help to identify the variables that have the greatest effect on exposure 29

estimates and help focus further information gathering efforts. 30

31

sequelae 32

These are a bnormal conditions that arise f ollowing the acute phase of a disease. For 33

example, kidney failure may follow acute E. coli O157:H7 infection

. 34

35

stakeholder 36

A stakeholder is a ny organization, governmental entity, or individual that will be 37

responsible for implementing, or financially aff ected by, new rules and regulations borne 38

of the risk assessment, or may be impacted by a given decision based on the risk 39

assessment. 40

41

s ubpopulatio n 42

This is a subset of the target population that has been identified for a specific purpose, 43

usually require s the ability to estimate an attribute of the subpopulation. 44

45

46Microbia l Risk Assessment Guideline Page 173

Draft

– Do not cite, circulate, or quote susceptible, sensitive, vulnerable 1

These terms refer to individuals or populations that for varying reasons suffer more 2

severe consequences than the general population as a result of exposure to a hazard

. 3

Although these terms are used interchangeably by many risk assessors and public health 4

experts, the Interagency Risk Assessment Consortium Susceptible Populations Workshop 5

considered the below definitions, but emphasized that when these terms are used, they 6

should be defined. 48 7

Susceptibility is: A capacity leading to higher risk at a given exposure level, due to 8

biological (intrinsic) factors that can modify the effect of a specific exposure 9

Sensitivity is: A capacity for higher risk due to t he combined effect of susceptibility 10

(biological factors) and differences in exposure 11

Susceptibility

- Includes intrinsic factors only ; Characteristic of an individual ; Defined by 12

the host 13

Vulnerability

- Includes intrinsic and extrinsic factors ; Characte ristic of an individual or a 14

group ; Defined by the host (behavior) and environment 49 15

16

transparency 17

This is c onducting a risk assessment in such a manner that all of the scientific analyses, 18

uncertainties, assumptions, and science policies which underlie the decisions made 19

throughout the risk assessment are clearly stated (i.e., made readily apparent). For risk 20

assessment to be transparent, methods, and assumptions should be clearly stated and 21

understandable to the intended audience, so that the audience is able to evaluate the 22

adequacy of the data and methods. 23

24

u ncertainty analysis 25

This is used to estimate the uncertainty associated with model inputs, assumptions, and 26

structure/form and t he process of interpreting the influence of uncertainty on the results 27

of a risk assessment

. 28

29

uncertainty factor 30

These are usually applied to accommodate for a lack of knowledge associated with inter

31

species extrapolation, high to low dose extrapolation (i.e., effect to no

- effect), population 32

variation (i.e., protection of s ensitive subpopulations), and extrapolation across exposure 33

durations (e.g., subchronic to chronic

. ) Although uncertainty factors are commonly 34

applied in chemical risk assessment, m uch less information is available supporting the 35

application of uncertaint y factors to microbiological risk assessment. 36

37

uncertainty 38

Uncertainty is imperfect knowledge of the microbiological hazard (e.g., its virulence), 39

environmental pathway/processes, or the human populations under consideration (from 40

MRA) 41

48 http://foodrisk.org/IRAC/events/2010

- 01

- 10/downloads/Concept_of_Susceptibility

- R_Parkin.pdf

49 In this document vulnerability is also used in the context of a ― vulnerability assessment,‖ which is not

related to the definitions discussed in this set of terms. Refer to Section 2.5.3 for the definition of

vulnerability with respect to the CARVER method.Page 174 Microbia l Risk Assessment Guideline

Uncertainty repres ents a lack of knowledge about factors affecting risk assessments and 1

can lead to inaccurate or biased estimates of risk and hazard. Some of the types of 2

uncertainty include scenario uncertainty, parameter uncertainty, and model uncertainty. 3

4

variabilit y 5

This r efers to the observed differences attributable to true heterogeneity or diversity in a 6

parameter. Examples include human physiological variation (e.g., natural variation in 7

body weight, height, breathing rate, drinking water intake rate), weather variability, 8

variation in soil types and differences in contaminant concentrations in the environment. 9

Variability is usually not reducible by further measurement of study, but it can be better 10

characterized. 11

12

virulence 13

This is t he degree of intensity of the disease produced by a microorganism as indicated 14

by its ability to invade the tissues of a host and the ensuing severity of illness. (see 15

pathogenicity for comparison) 16

17

18

19

20Microbia l Risk Assessment Guideline Page 175

Draft

– Do not cite, circulate, or quote 

## 10. ABBREVIATIONS 1

2

ACSSuT Ampicillin, chl oramphenicol, streptomycin, sulfonamides, and tetracycline 3

AFLP Amplified fragment length polymorphism 4

AIPH Army Institute of Public Health 5

ALOP Appropriate level of protection 6

AOAC Association of Analytical Communities 7

APHIS Animal and Plant Health Insp ection Service (U.S. Department of 8

Agriculture) 9

ASM American Society for Microbiology 10

Bcc Burkholderia cepacia complex 11

BenMAP Environmental Benefits Mapping and Analysis program 12

BSE Bovine Spongiform Encephalopathy 13

°C degrees Celsius 14

CART Classification an d regression tree 15

CARVER Criticality, Accessibility, Recuperability, Vulnerability, Effect, and 16

Recognizability 17

CDC Centers for Disease Control 18

CDER Center for Drug Evaluation and Research (Food and Drug Administration) 19

CEA cost

- effectiveness analyses 20

CFSA N Center for Food Safety and Applied Nutrition 21

CFR Code of Federal Regulations 22

cfu c olony forming unit 23

DALYs Disability

- adjusted life years 24

DHS Department of Homeland Security 25

DNA Deoxyribonucleic Acid 26

DOD Department of Defense 27

DT104 Definitive type 104 (p 48) 28

ECSSC European Commission Scientific Steering Committee 29

EPA Environmental Protection Agency 30

° F degrees Fahrenheit 31

FAO Food and Agriculture Organization 32

FDA Food and Drug Administration 33

FIRRM Foodborne Illness Risk Ranking Model 34

FSIS Food Safety and Inspection Service 35

GAO U.S. Government Accountability Office 36

GRAS generally recognized as safe 37

gyrB DNA gyrase, subunit B 38

HACCP Hazard Analysis Critical Control Point 39

HC Hazard Characterization 40

HI Hazard Identification 41

HIV Human immunodeficiency virus 42

HYE s Healthy

- years equivalents 43

ID 50 Median Infectious dose 44

ILSI International Life Science Institute 45

LD 50 Median lethal dose 46Page 176 Microbia l Risk Assessment Guideline

LT2ESWTR Long Term 2 Enhanced Surface Water Treatment Rule 1

MAC Mycobacterium Avium

- Complex 2

MC M C Markov Chain Monte Carlo Simulation 3

MILYs Morbidity Inclusive Life Years 4

MLGT Multilocus genotype sequencing 5

MLST Multilocus sequence typing 6

MRA m icrobial risk assessment 7

MRM microbial risk management 8

mRNA messenger ribonucleic acid 9

NASA National Aeronautics and Space Administration 10

NCEZID National Center for Emerging and Zoonotic Infectious Diseases 11

NCRP National Committee on Radiation Programs 12

NIOS H National Institute for Occupational Safety and Health (Centers for Disease 13

Control) 14

NN Neural Networks 15

NOAEL No observable adverse effect lev el 16

NRC National research council 17

OAQPS Office of Air Quality Planning and Standards 18

OECD Organization for Economic Cooperation and Development 19

OMB Office of Management and Budget 20

OPP Office of Pesticide Programs (US Environmental Protection Agency) 21

OPPT Of

fice of Pollution Prevention and Toxics (US Environmental Protection 22

Agency) 23

ORD Office of Research and Development (US Environmental Protection 24

Agency) 25

OSA Office of the Senior Advisor (US Environmental Protection Agency) 26

OW Office of Water (US Environmen tal Protection Agency) 27

P/CC Presidential/Congressional Commission 28

PCR Polymerase chain reaction 29

PCR

- RFLP Polymerase chain reaction

- restriction fragment length polymorphism 30

PFGE Pulsed field gel electrophoresis 31

PM 2.5 Particulate Matter with a diameter smal ler than 2.5 microns 32

QALYs Quality

- adjusted life years 33

RAF Risk Assessment Forum (US Environmental Protection Agency) 34

RAPD Random amplification of polymorphic DNA 35

rDNA Ribosomal deoxyribonucleic acid 36

REP

- PCR Repetitive element polymerase chain reaction 37

R&D Research and development 38

SARS Severe Acute Respiratory Syndrome 39

SDWA Safe Drinking Water Act 40

SNP Single nucleotide polymorphism 41

TAMU Texas A&M University 42

TB Tuberculosis 43

TCCR Transparency, Clarity, Consistency, and Reasonableness 44

U.S. United States 45

USDA U .S. Department of Agriculture 46Microbia l Risk Assessment Guideline Page 177

Draft

– Do not cite, circulate, or quote VBNC Viable

- but

- not

- culturable 1

vCJD variant Creutzfeldt

- Jacob Disease 2

VOI V alue of information 3

WHO World Health Organization 4

5Page 178 Microbia l Risk Assessment Guideline



## 11

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23Microbia l Risk Assessment Guideline Page A

- 1

Draft

– Do not cite, circulate, or quote 

## Appendix A Example Assumptions 1

2

This appendix contains a list of types of assumptions that you may encounter during 3

MRA. Many of these assumptions are due to data gaps. As data become available in the 4

future, these assumptions could be differen t. Whether any of these assumptions are 5

justifiable and adequate for a given risk assessment can be decided on a case

- by

- c ase 6

basis. For example the assumptions about the immune status of the population may be 7

simple (no one has immunity) or complex (bas ed on actual immunity data). 8

9

For all assumptions, the strengths, limitations, and implications of the assumption should 10

be fully explored and documented in the risk assessment. This level of transparency 11

helps peer reviewers decide if they agree with the basis of the assumptions and whether 12

the results of the risk assessment seem credible. 13

14

Some assumptions are related to the scope of the risk assessment and are less subject to 15

challenge on a scientific basis. For example the choice to evaluate a single agent instead 16

of a mixture of agents is a policy decision about scope. In addition most MRA is limited 17

in scope to estimating risk as a result of a single exposure event. In chemical risk 18

assessment the exposure duration is often a 70

- year life span. 19

20

G eneral Assumptions: 21

22

MRA’s focus on known pathogens that contribute significantly to the human disease 23

burden or emerging pathogens for which the potential for disease is recognized, but the 24

disease burden is unknown. 25

26

MRA’s related to food

- borne infection s typically focus on a food

- pathogen pair. 27

This assumes that a specific pathogen is associated with a specific food. In reality, more 28

than one pathogen could be associated with a specific food (for example, Salmonella and 29

Campylobacter in chicken) and al so different types of foods could be associated with a 30

specific pathogen (for example, Salmonella in chicken and pork). 31

32

A given MRA’s scope reflects the regulatory jurisdiction of the sponsoring agency 33

and the statues behind regulations . For example EPA risk assessments in water media 34

are divided into ambient water which is regulated by the Clean Water Act and drinking 35

water which is regulated by the Safe Drinking Water Act. EPA has not to date sponsored 36

a MRA that examined the risks of one pathogen in a ll water media. MRA for foods 37

categories that are regulated by multiple agencies have required interagency 38

collaborations. 39

40

A mathematical model is assumed to adequately represent complex biological 41

phenomenon and ecological relationships. 42

43

Available dat a are assumed to be representative of the parameter

. In practice, this 44

assumption can lead to an overestimate or underestimate. Although MRA‘s routinely 45Page A

- 2 Microbia l Ris k Assessment Guideline

incorporate uncertainty (and/or variability) about the parameters of interest, the 1

underlying assumpt ion is that data included in a risk model is representative of the 2

pathogen and target population of interest. Many examples illustrate the ubiquitous 3

nature of this assumption. D ata from experiments using animal models are commonly 4

used rather than data from human infections. Certain related bacteria are considered to 5

be surrogates for the pathogen of interest. Data based on short

- term exposures are 6

extrapolated to model the chronic effects of prolonged exposures. Public health 7

surveillance data is co nsidered to be reflective of the actual disease burden experienced 8

by the human population under surveillance. Surveys of foods reflect the true prevalence 9

and distribution of pathogens in that food. Surveys that focus on individual‘s food 10

consumption du ring a limited time period reflect the long

- term consumption patterns of 11

the individual, and the study population is representative of the population of interest. 12

13

It is assumed that it is appropriate to pool data derived from a variety of sources. 14

This as sumption can result in an overestimate or underestimate of true risk

. Estimated 15

prevalence and cell number distribution of pathogens is generally determined by 16

combining results available in published literature, government‘s surveillance reports and 17

indus try reports. These data have inherent variability and uncertainty resulting from the 18

variety of methods used to obtain them. It is assumed that pooling data from multiple 19

sources (with or without weighting each observation with respect to a quality score ) will 20

provide a valid estimate of the parameter of interest with appropriate limits of 21

uncertainty. 22

23

Normal distributions or triangle distributions are often assumed for parameters

. 24

P oint estimate based on 50 th

, 75 th

, or 95 th percentile are sometimes as sumed and may not 25

be correct or appropriate

. 26

27

Assumptions concerning the Agent: 28

29

It is generally assumed that a minimum unit of the microbiological hazard is 30

necessary to cause disease. For example, a risk model could be based on an underlying 31

assumption that a single cell of a pathogenic bacterium has the potential to cause disease. 32

Alternatively, it could be assumed that some minimum concentration of a bacterial toxin 33

is required to cause disease. 34

35

Most models assume that each microbial unit acts indepe ndently, and that a single 36

bacterial cell or viral particle has the potential to cause disease. 37

38

It is usually assumed that the chance of contracting a disease once an individual is 39

infected is independent of the ingested dose , i.e., once infected, a parti cular individual 40

contracts the disease regardless of ingested dose. A higher dose of pathogen would not 41

cause more severe symptoms. 42

43

It is often assumed that factors intrinsic to the pathogen are the primary 44

determinant of the agent’s ability to cause inf ection/disease. Extrinsic factors (related 45

to both the environment and the potential host) also impact the affect the pathogen‘s 46Microbia l Risk Assessment Guideline Page A

- 3

Draft

– Do not cite, circulate, or quote ability to cause infection/disease, and in some cases may be a more important 1

determinant of the health outcome than the agent itself. 2

3

Certain assumptions must be stated regarding the variability among pathogen 4

subtypes with respect to multiple characteristics that influence the development 5

and/or detection of infection and/or disease. Many characteristics intrinsic to a 6

specif

ic pathogen ultimately influence both the probability that a specific subtype of a 7

pathogen will result in an infection (or development of disease) and that this 8

infection/disease will be detected and subsequently reported. These characteristics must 9

be c onsidered and prioritized according to their relative importance in impacting the 10

health outcome of interest. In some cases, data is available that permits differentiation 11

among subtypes with respect to these factors during the risk assessment process. F or 12

example, certain Salmonella serotypes appear to be more pathogenic to humans than 13

others, and their relative pathogenicity could be defined to vary between serotypes in risk 14

models. In other cases, these data are not available and it becomes necessary to assume 15

that all subtypes are equal with respect to the defined characteristic. For example, certain 16

Salmonella serotypes could be assigned the same relative pathogenicity in risk models 17

when information that discriminates among the serotypes is unavail able. It is common to 18

assume all isolates in the scenario are equally pathogenic. 19

20

Certain assumptions must be stated regarding the variability among pathogens and 21

pathogen subtypes with respect to survival and growth in a variety of matrices. For 22

food ma trices in particular, m any characteristics intrinsic to a specific pathogen 23

ultimately influence the ability of the pathogen to survive and/or grow in that matrix

. 24

These characteristics must be considered and prioritized according to their relative 25

import ance in impacting the role of a given food as a vehicle. Assumptions concerning 26

these characteristics are often necessary. For example, the pathogen may not be able to 27

proliferate in the food, but it can survive and cause an adverse effect when consumed b y 28

a susceptible host. Some pathogens can form spores to ensure survival during adverse 29

environmental condition and other may produce toxins under certain circumstances. 30

31

It is assumed that a vailable growth kinetics models are adequately representative of 32

a ll pathogen subtypes and are appropriate across different food matrices/environmental 33

conditions. This assumption can lead to an overestimate or underestimate of risk. 34

35

Assumptions concerning the Host: 36

37

Certain assumptions are required with respect to the variability of susceptibility of 38

individuals to the development of infection/disease. For example, it is frequently 39

assumed that certain groups of people are more susceptible to infection/disease than 40

others (i.e., young, elderly, pregnant woman, immuno c ompromised individuals). 41

42

Target population is assumed not to be vaccinated or immune due to previous 43

exposures

. The probability of infection or illness resulting from exposure is independent 44

of previous exposures; also, the probability of infection or il lness resulting from 45Page A

- 4 Microbia l Ris k Assessment Guideline

secondary transmission is also independent of previous exposures. This ignores the 1

possibility of temporary or permanent immunity. 2

3

Assumptions concerning the Environment: 4

5

Risk assessment models typically assume that microbes are hom ogeneously 6

distributed throughout the specified matrix

. While this may be a reasonable 7

assumption for certain foods (e.g., milk, juices) and air, and water , it is unlikely to hol d 8

true for most food categories or soil matrices. 9

10

MRA models typically assum e that the quantitative levels of contamination (i.e. 11

microbiological counts) are best represented by a log normal distribution. 12

13

Assumptions are stated concerning the analytical methods used to detect the 14

presence/quantitative level of the pathogen in a m atrix. It is typically assumed that 15

viable pathogen can be detected (e.g., culture, bioassay, serological test, polymerase 16

chain reaction [ PCR ] ). It is necessary to indicate the limit of detection, analytical 17

specificity, epidemiologic sensitivity, and ep idemiologic specificity of a described 18

method when interpreting the results obtained when testing to identify a particular 19

pathogen in a defined matrix . It is often assumed that all subtypes of a pathogen are 20

equally likely to be detected using a particul ar analytical method; however, this may not 21

be valid. Similarly, certain methods are applied across a variety of matrices. In some 22

situations, characteristics of the matrix itself may interfere with pathogen detection. 23

24

Assumptions concerning the geograp hic and temporal (seasonal) distribution of 25

pathogens are required. For example, it is believed that E. coli O157:H7 exhibits a 26

seasonal distribution in cattle

, ground beef , and ambient waters

. You could assume that 27

this pathogen is more prevalent during May

– September than in October through March 28

when modeling potential exposures or you could assume that prevalence is uniform 29

throughout the year. You could make similar assumptions concerning the geographic 30

distribution of a pathogen. 31

32

The complex seri es of environmental events that impact the survival and/or growth 33

of microbiological hazards are typically simplified in the context of risk models. 34

Considerable variability exists with respect to environmental conditio ns (i.e., time, 35

temperature, pH ) ove r time. For example, conditions associated with storage, 36

refrigeration, product formulation, and batching process will vary greatly. Risk models 37

assume that a single situation (or perhaps a limited number) occurs and that this factor 38

occurs consistently over time. 39

40

Assumptions Concerning the Exposure Scenario: 41

42

The exposure time span of interest is usually specified in the scope. Whether the 43

exposure time span is a specified event (e.g., meal, trip to the beach) or a lifetime it is still 44

an assumption that must be transparent. 45

46Microbia l Risk Assessment Guideline Page A

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Draft

– Do not cite, circulate, or quote A specific food is the vehicle of transmission for a given pathogen. In reality, multiple 1

foods may serve as a vehicle. Further, assumptions regarding the relative importance of 2

potential exposures (i.e., food

- borne; direct con tact with animals, wildlife, insect vectors, 3

or the environment; human

- to

- human transmission; water

- borne exposures, recreational 4

exposures) are necessary. 5

6

Certain assumptions are required with respect to individuals’ exposures to 7

pathogens of concern and the variability in exposures among individuals

. These 8

assumptions may lead to an underestimate or overestimate of risk. Consumer behaviors 9

are diverse (e.g., by region, ethnicity, religion, food preparation, eating practices, 10

packaging methods, manufactur ing production practices, food production practices, local 11

conditions, sanitation). Also, foods are not going to be consumed with the same 12

frequency by the same people over an entire year. Factors such as seasonal availability of 13

certain foods and changi ng eating habits may be appropriate to consider in a national 14

scale MRA. It is not feasible or advisable to try to break out every possible behavior that 15

may influence the exposure scenario. Often an average behavior is assumed to be 16

representative at a population level. For example the number of servings consumed by 17

each person or the number of contaminated servings. 18

19Microbia l Risk Assessment Guideline Page B

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Draft

– Do not cite, circulate, or quote 

## Appendix B Hazard Identification Questions 1

2

This appendix contains exampl es of specific hazard identification questions that may be 3

useful for the risk assessor ‘ s consideration

. These are not all the questions risk assessors 4

might consider. 5

6

General Questions: 7

8

1. What is the hazard in question and what is the specific media or f ood of concern? 9

(e.g. Campylobacter spp

. in poultry). 10

2. What are the common routes of exposure associated with the hazard? 11

3. Are there indicator organisms or surrogates that can allow for an indirect 12

evaluation of this pathogen in the absence of data? 13

4. Which p athogens are of concern to public health? Which are regarded as being of 14

greatest concern, and why? 15

5. Are any media closely associated with, or often linked to, specific illnesses? 16

6. What agents are present in the media that may cause adverse health effects? 17

7. P rioritize and tabulate all the pathogens in terms of their degree of severity

. 18

8. How is the media linked to the illness associated with the pathogen 19

(epidemiologic evidence? Laboratory evidence?) 20

9. Are there available epidemiological data and microbiological data to associate 21

what type of pathogen is associated with the media of interest ? 22

10. Are there adequate public health data to substantiate the occurrence of pathogenic 23

microorganisms in the media in question? 24

11. Are there any established standards/guidelines reg arding the pathogen of interest ? 25

(assuming there are answers) 26

12. Is there data relevant to support the hazard? scientific literature? databases from 27

industry, government agencies, international organizations? expert opinion? 28

clinical studies, epidemiological studies, surveillance studies, outbreaks (domestic 29

and international)? laboratory animal studies, investigations of the characteristics 30

of the food

- borne pathogen? microbial ecological studies? microorganism‘s 31

behavior studies? sensitive populations (high risk, elderly, pre natal)? 32

13. For food media, i s there a list of generic processes for the normal exposure of the 33

food product in question in the food chain? retail, restaurant, HACCP plan 34

systems? 35

36

Questions concerning the Agent: 37

38

1. What type of pathogen is this organism (Bacteria, Virus, Parasite, Fungus, Prion

, 39

noninfectious toxin)? 40

2. What are other taxonomic/strain considerations that influence ability to cause 41

disease? Does the pathogen have particular strains that differ in ability to cause 42

disease? If so, what is the strain of interest? 43

3. What intrinsic properties influence this agent‘s ability to cause disease? 44

4. What is the ―life cycle‖ of this agent? 45Page B

- 2 Microbia l Risk Assessment Guideline

5. Does the pathogen produce a toxin? If so, is it the toxin that presents the hazard, 1

or the pathogen? 2

6. A re there indicator organisms or surrogate species that can allow for an indirect 3

evaluation of this agent in the absence of data? 4

7. How is the pathogen identified? 5

8. What factors influence the spatial distribution of this agent (clumping, 6

aggrega tion, particl es, clustering )? 7

9. Is the pathogen an anaerobic, gram

- positive, spore

- forming rod that produces a 8

toxin? 9

10. Is the pathogen a microaerophilic organism? 10

11. What are the biochemical/taxonomic characteristics to identify the pathogen? 11

12. What is the subtyping of the pa thogen? 12

13. What are the survival characteristics? heat resistance? susceptibility to 13

antimicrobial food additives? acid resistance? sensitivity to disinfectants or 14

desiccation ? sensitivity to radiation? UV? ionizing? 15

14. Are there phenotypic characteristics that influence virulence and/or 16

pathogenicity? 17

15. Are there genotypic characteristics that influence virulence and/or pathogenicity? 18

16. Are the spores heat resistant? Can it survive in treatment processes? 19

17. Is there a microbiological testing/identification method fo r the pathogen in human 20

clinical samples? Is there a specific method for testing for this pathogen? 21

18. What methods are available for detecting and quantifying the agent? 22

19. What are the cultural characteristics? 23

20. What are the detection methods to identify the food

- borne pathogen? 24

21. What are the sampling and enrichment techniques to identify the food

- borne 25

pathogen? 26

22. What are the growth characteristics? free

- living vs. obligate parasite? growth 27

requirements? temperature? pH? water activity? oxygen? Relative humidi ty? 28

23. What is the main pathogenic strain of this hazard, and what other specie(s) within 29

the identified genus are humans susceptible to? (e.g.

, Campylobacter jejuni and 30

coli ). 31

24. Are there phenotypic characteristics that influence this agent‘s virulence and/or 32

pathogenicity? 33

25. Are there genotypic characteristics that influence this agent‘s virulence and/or 34

pathogenicity? 35

26. How does the agent cause pathology and/or disease? 36

27. Does the microorganism produce a toxin while growing in the intestinal tract? 37

28. How infectio us/toxigenic is the pathogen? 38

29. What is the incubation period? 39

30. What is the pathogenic microorganism‘s virulence? 40

31. What is the mechanism of action for infection and illness? 41

32. Is secondary transmission possible? 42

33. What is the target organ? 43

34. Can the pathogen repli cate in the media of concern? 44

35. Is the pathogen's ability to cause disease restricted to specific strains with 45

identifiable virulence characteristics? 46Microbia l Risk Assessment Guideline Page B

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Draft

– Do not cite, circulate, or quote 36. What are the virulence

- associated characteristics to identify the pathogen? 1

37. What is the pathogenicity? di s. char. and diagnosis? sequelae? host range? 2

infectious dose? subpopulations at risk? animal models? 3

38. Describe the contagiousness of the infection (is or is not) 4

39. Identify the degree of severity for each pathogen 5

6

Questions concerning the Host: 7

8

1. Who is the susceptible population and what makes them susceptible ? (e.g. 9

childr en, adults, pregnant women ) 10

2. What types of hosts can be infected by this agent? Is there host specificity? 11

3. Are any practices /behaviors closely associated with, or often linked to, specif ic 12

illnesses? 13

4. Is illness host

- specific, i.e. more likely to affect susceptible populations? 14

5. Are there subpopulations at increased risk for this pathogen? 15

6. What are the demographics associated with this pathogen? 16

7. What are the general socioeconomic trends as sociated with this pathogen? 17

8. What is/are the characteristic(s) of the infection caused by the above hazard? (e.g. 18

gastroenteritis). 19

9. What is the nature of identified cases of infection? (e.g.

, sporadic, small/family 20

related outbreaks). 21

10. What types of patho logy and/or diseases are caused by this agent? 22

11. What are the typical symptoms of illness, and how do these differ between 23

different pathogens? 24

12. What are the symptoms? 25

13. Is the microbe presenting itself as a gastrointestinal symptom? Vomiting, watery 26

diarrhea? inflammatory diarrhea? 27

14. Does the pathogen produce all or any of the following symptoms: abdominal 28

cramps, nausea, vomiting, diarrhea, fever, dehydration? 29

15. Does the microbe usually have no manifestations of gastrointestinal illnesses 30

associations? neurolog ic? systemic? hepatitic? 31

16. What is the incubation period within susceptible host, before the onset on an 32

infection? 33

17. Is the pathogen found in the large intestinal tract only? 34

18. Does it shed in the feces? 35

19. What is the associated morbidity/mortality? 36

20. How might one define immunocompromised? 37

a) Are there specific quantifiable biomarkers of immunocompetency? 38

b) Can one define such biomarkers? 39

21. How would one account for genetic/ethnic/cultural differences within or between 40

populations? 41

22. What is the definition of biomarkers for malnutrition? 42

23. What are the age groupings ? How do they relate to susceptibility? 43

24. Should age be defined with respect to chronology or physiology? 44

25. What would one do if the definitions [for different biomarkers] overlap? 45

26. What are the definitions for chron ic ailments? 46Page B

- 4 Microbia l Risk Assessment Guideline

27. What are multiple concurrent factors? 1

28. How is immune status defined? 2

3

Questions concerning the Environment: 4

5

1. What are the environmental conditions of the pathogen in question: 6

contamination, growth, inactivation, elimination, survival, for each part of the 7

exposure scenario ? 8

2. Does the pathogen have any specific survival characteristics that may promote its 9

survival through the exposure scenario? 10

3. How do various environmental conditi ons (temperature, nutrients, pH ) influence 11

this agent‘s growth, su rvival, and/or death? 12

4. Are there certain environmental conditions and/or control processes that this agent 13

can survive (or develop resistance)? 14

5. Are there seasonal, geographic or climatic factors that affect the occurrence of 15

this agent? 16

6. What are the time fa ctors involved in identifying the pathogen? 17

7. What are the temperature factors involved in identifying the pathogen? 18

8. What is the geographical range and seasonality of the pathogen? 19

9. Is the organism found in warmer months? 20

10. What is the geographic location? 21

11. What season does it occur? 22

12. If carried by another species, what is the geographic range of that species? 23

13. Is there a reservoir for the pathogen? 24

14. What are the ways in which the pathogen can contaminate the media? 25

15. Is that pathogen zoo notic ? 26

16. Where (in what media?) is the pathogen commonly found? 27

17. Is the pathogen endemic? 28

18. Has it been identified in other countries recently? 29

19. What is the reservoir and/or environmental niches for this agent? Are there other 30

important ecologic factors that influence this agent? 31

20. How can th e media become contaminated with this pathogen? Are there any 32

specific practices /behaviors that could promote survival of this pathogen? 33

21. For food, d id contamination occur during growing, harvesting, processing, 34

storing, shipping, or final preparation? 35

22. Doe s the pathogen grow in the media ? 36

23. What is the route of contamination and location of the pathogen in the media ? 37

24. What is the microbial ecology to identify the pathogen? 38

25. What are the technologies associated with identifying the pathogen? 39

26. What are the consum er trends associated with identifying the pathogen? 40

27. For food, what is the effect of food processing and preparation on their survival? 41

28. What is the consumption patterns associated with this pathogen? 42

29. What are the marketing and preparation practices associat ed with this food

- borne 43

pathogen? 44

30. What are the globalization trends associated with this pathogen regarding the 45

media of interest ? 46Microbia l Risk Assessment Guideline Page B

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Draft

– Do not cite, circulate, or quote 31. Is the pathogen's presence in contaminated media the result of an error or 1

breakdown in normal controls? 2

32. What are the consum er behavior and consumption practices associated with this 3

pathogen and media ? 4

33. For food, i dentify and list (document) all the associated places (poultry (chicken, 5

turkey), slaughter house (bovine, swine) , ready to eat, ...), locations/residing 6

critical cont rol points, and the time that certain pathogens are present. 7

34. What is the principal reservoir of this hazard, and where is it also commonly 8

found? 9

35. What are common environmental media and the expected concentration of this 10

agent? 11

36. Is the pathogen found in h umans or animals or both? 12

37. Where is the source found? humans? domestic wild animals? poultry/eggs? 13

swine? cattle? rodents? pets such as turtles, chicks, dogs and cats. 14

38. Is the pathogen found in cat, rodent or bird feces? 15

39. Is the food

- borne pathogen found in s oil? dust? sewage? and intestinal tracts of 16

animals and/or humans? 17

40. What is the pathogen‘s reservoir in nature? 18

41. Is the pathogen found in the intestinal tract? 19

42. Identify and list all the potential sources (reservoir) for all of these pathogens. 20

21

Questions co ncerning Transmission: 22

23

1. What is the mode of transmission? (e.g.

, direct contact with contaminated 24

animals/carcasses, ingestion of contaminated food or water). 25

2. Identify all the potential routes/pattern of transmission (direct or indirect ) of 26

microbial infe ction (disease) for each pathogen 27

3. Is the pathogen found in putting something in the mouth that has been 28

contaminated with the stool of an infected person or animal; direct contact with 29

the droppings of infected animals? 30

4. Is the pathogen a person

- to

- person transmission such as in child daycare settings? 31

5. For food media, w hat are the eating habits associated with the pathogen? 32

6. How does this agent infect a susceptible host? What are the typical routes of 33

infection and/or portals of entry? 34

7. Is there potential fo r secondary spread? 35

8. What is the infectious dose? 36

37

38