CONTRIBUTORS

Draft Risk Assessment on the

Public Health Impact of

Vibrio parahaemolyticus

in Raw Molluscan Shellfish

Center for Food Safety and Applied Nutrition

Food and Drug Administration

U.S. Department of Health and Human Services

December, 2000

Prepared by the Vibrio parahaemolyticus

Risk Assessment Task Force
CONTRIBUTORS

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CONTRIBUTORS

The Vibrio parahaemolyticus risk assessment was performed and this document prepared by the

following task force members:

Team Leaders

Marianne Miliotis, William Watkins

Harvest

Marleen Wekell (Lead)

Atin Datta, Elisa Elliot, Walter Hill, Charles Kaysner, Brett Podoski

Post Harvest

Angelo DePaola, David Cook (Co-leads)

George Hoskin, Susan McCarthy, William Watkins

Public Health

John Bowers (Lead)

Epidemiology

Marianne Ross (Segment lead)

Karl Klontz, Debra Street, Babgaleh Timbo

Consumption

Michael DiNovi

Dose-Response

Donald Burr (Segment lead)

John Bowers, Mahendra Kothary, Wesley Long, Marianne Miliotis,

Ben Tall, Mark Walderhaug

Modeling

John Bowers (Lead), Mark Walderhaug
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ACKNOWLEDGEMENTS

The V. parahaemolyticus Task Force greatly appreciates the efforts of all the following people

in providing us with comments, information and assistance we needed to accomplish this risk

assessment:

Haejung An (Oregon State University)

Fred Angulo, Mary Evans, Nicholas Daniels, Paul Mead and Malinda Kennedy (Centers for

Disease Control)

Robert Buchanan (FDA/CFSAN)

Mercuria Cumbo (Department of Marine Resources, Maine)

Sherri Dennis (FDA/CFSAN)

Paul Distefano (FDA/CFSAN)

Robin Downey (Pacific Coast Shellfish Growers Association)

Jan Gooch (National Oceanographic Service)

Michael Kelly (University of British Columbia)

Bill Kramer (Environmental Protection Agency)

Ken Moore, Sandra Sharp (Interstate Shellfish Sanitation Conference)

Mitsuaki Nishibuchi (Kyoto University, Japan)

Gary Richards (United States Department of Agriculture/Agricultural Research Services)

Tina Rouse (FDA/CFSAN)

Angela Ruple (National Marine Fisheries Service)

Patricia Schwartz (FDA/CFSAN)

FDA Regional Shellfish Experts

FDA Shellfish Sanitation Team

Molluscan Shellfish Institute

National Advisory Committee for Microbiological Criteria for Food (NACMCF)

Shellfish Industry

State Shellfish Experts

State Health Departments

The Task Force would especially like to thank the FDA/CFSAN Offices and Risk Assessment

Consortium members for intensive review of the document in December, as well as Federal

employees from other agencies, and Special Government Experts, for review of the document in

May. We are also deeply grateful to Lauren Posnick for her outstanding contribution in

preparing the interpretive summary of this document, Carolyn Jeletic for excellent technical

editing of this document, and Faye Feldstein for assisting with assembling all the references.
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COMMENTS AND INFORMATION REQUESTED DURING

PUBLIC COMMENT PERIOD

The Food and Drug Administration has always committed itself to bringing the best science

available to all of its decisions and activities, which is the underlying reason that this and other

quantitative microbial risk assessments are undertaken. As with any risk assessment, the

knowledge available for the conduct of the current risk assessment was incomplete and

assumptions had to be made when specific data were lacking or limited in scope. When such

assumptions were made, we have attempted to seek the best scientific information available

including having all assumptions and modeling approaches reviewed by the National Advisory

Committee on Microbiological Criteria for Foods and the interagency Risk Assessment

Consortium. However, to ensure that we have both identified all key data sources and

submitted the assessment to a rigorous peer review, we are releasing the assessment in draft

form. A comment period has been established during which we will be actively seeking

comments, suggestions, and additional data sources. Written comments should be submitted to

the Dockets Management Branch (HFA-305), Food and Drug Administration, 5630 Fishers

Lane, Room 1061, Rockville, MD 20852 within 60 days after publication of the document.

Two copies of comments are to be submitted, except that individuals may submit one copy.

Comments must be identified with the Federal Register Docket No. 99N-1075. Some of the key

areas that we are seeking comments are listed below. The information acquired during the

comment period will be reviewed and used, as appropriate, to further enhance the risk

assessment and decrease uncertainty to the greatest degree possible. As stated in the Federal

Register, Docket No. 99N-1075, the preliminary results of the draft V. parahaemolyticus risk

assessment will be presented at a public meeting during the comment period.

Examples of specific assumptions made in the risk assessment for which we are seeking

comments and additional data include:

• Pathogenesis was based on the presence of the most characterized virulence factor of the

organism, thermostable direct hemolysin (TDH). Based on the levels from the

literature, levels of pathogenic V. parahaemolyticus used ranged from 1.6% to 6.4% for

the West Coast; 0.1% to 0.4% remaining regions.

• Time after harvest to refrigeration was based on a 1997 GCSL survey that included

dealer reported statistics on the length of harvest (28), as well as on the NSSP timetemperature control plan.

• All V. parahaemolyticus in oysters, regardless of pathogenicity, have similar growth and

survival rates.

• The growth rate of V. parahaemolyticus in oysters is approximately 1/4 that of the

growth rate in broth at all environmental temperatures.

• Lag time to growth of V. parahaemolyticus in oyster after harvest is negligible.
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• The growth rate of V. parahaemolyticus drops uniformly down to zero during the period

of initial refrigeration following harvest.

• Depuration is ineffective against V. parahaemolyticus in oysters.

• Equal consumption patterns were assumed between immune compromised and healthy

populations.

• Equal virulence was assumed for all pathogenic V. parahaemolyticus.

• The dose-response relation was shifted by 1 log10 from that based on published clinical

trials, in order to achieve a relationship that was more reflective of the CDC disease

estimates.

The FDA is also seeking comments on the appropriateness of the risk assessment model and the

parameters used to develop the model.

• Preliminary modeling of the Harvest Module did not show a great effect of salinity on

the levels of pathogenic V. parahaemolyticus, within the range that oysters are normally

harvested. Therefore this parameter was not included in our simulations.

• The study by DePaola et al., 1990. (Reference number 38) was deemed to be most

appropriate for the purpose of this risk assessment, because this study was the most

inclusive of seasonal changes in V. parahaemolyticus density in oysters from major

oyster producing areas representative of the Pacific, Gulf and Atlantic Coasts. Are there

other studies that should have been considered?
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EXECUTIVE SUMMARY

The Food and Drug Administration (FDA) conducted a risk assessment to characterize the

public health impact associated with consumption of raw oysters containing pathogenic Vibrio

parahaemolyticus. This effort was initiated in January 1999, in response to four outbreaks

occurring in the United States in 1997 and 1998 involving over 700 cases of illness, the majority

of which were associated with the consumption of raw oysters. These events renewed concern

for this pathogen as a serious foodborne threat to public health, given that the last outbreak in

the United States occurred in 1981. In two of the 1998 outbreaks a serotype previously reported

only in Asia, O3:K6, emerged as a principal cause of illness in the United States for the first

time. The outbreaks also introduced uncertainty about the effectiveness of current criteria for

closing and reopening shellfish waters to harvesting, and about previous FDA guidance

indicating that no more than 10,000 V. parahaemolyticus per gram should be present in

shellfish.

Input for this risk assessment was obtained from many sources, including both published and

unpublished scientific literature and reports, State shellfish control authorities, the Centers for

Disease Control and Prevention (CDC), the shellfish industry, the Interstate Shellfish Sanitation

Conference (ISSC), and records from State Health Departments. During the development of

this risk assessment model, information that was lacking was identified, and these gaps

necessarily required certain assumptions to be made. The National Advisory Committee on

Microbiological Criteria for Foods (NACMCF) reviewed each significant assumption included

in the risk assessment.

The objectives of the risk assessment were twofold. One was to produce a mathematical model

of the risk of illness incurred by consumers of raw oysters containing pathogenic V.

parahaemolyticus. The second objective of this quantitative risk assessment was to provide

FDA with information that will assist the agency with the review of current programs relating to

the regulation of Vibrio parahaemolyticus in raw molluscan shellfish to ensure that such

programs protect the public health. To accomplish this, the project sought to achieve the

following: (a) evaluate the current criteria used for closing and reopening shellfish waters to

harvesting, (b) evaluate preventive and intervention measures for controlling V.

parahaemolyticus in oysters after harvest, and (c) evaluate the current guidance of 10,000 viable

V. parahaemolyticus per gram of shellfish.

In order to develop the model, the risk assessment was divided into three modules, Harvest, Post

Harvest, and Public Health. The Harvest and Post Harvest Modules differentiated two distinct

time frames that affect V. parahaemolyticus levels in oysters harvested for raw consumption.

Significant differences in oyster harvesting methods, handling practices, and climates in the

United States were sufficient to distinguish five separate geographic regions (Northeast

Atlantic, Mid-Atlantic, Pacific Northwest, Louisiana Gulf Coast, and the remaining Gulf Coast)

and four seasons, and these were treated separately in modeling each of the modules.

The Harvest Module incorporated factors influencing the prevalence of V. parahaemolyticus in

oysters up to the time of harvest, and identified the parameters that contribute to the likelihood
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that shellfish in a growing area will contain pathogenic strains of V. parahaemolyticus.

Quantitative modeling in this module used water temperatures as factors influencing and,

therefore, potentially predicting the prevalence of pathogenic V. parahaemolyticus in the harvest

waters and oysters.

The Post Harvest Module addressed factors associated with handling and processing of oysters

after harvest, in particular those that could influence the levels of V. parahaemolyticus in

oysters at consumption. Such factors included in the modeling of this module were ambient air

temperatures at time of harvest, time from harvest that oysters remained unrefrigerated, time

required to cool oysters once placed under refrigeration, and the length of time oysters are

stored in refrigeration until consumption. This module also simulated intervention measures

that may affect V. parahaemolyticus densities, such as cooling immediately after harvest,

freezing, and use of mild heat treatment (5 min at 50 °C) .

The Public Health Module estimated the number of illnesses based on the levels of pathogenic

V. parahaemolyticus at consumption, which was determined from the Harvest and Post Harvest

modules. This module was further subdivided into three segments, epidemiology, consumption,

and dose-response. The epidemiology segment included the number of illnesses, the severity

and type of illness, the population affected, and the seasonal incidence. The consumption

segment considered the number of oyster meals/servings eaten, the quantity of oysters

consumed per serving, and the levels of total and pathogenic V. parahaemolyticus in the

shellfish at consumption. The dose-response segment related the actual levels of V.

parahaemolyticus consumed with frequency and severity of illness.

Outputs from the risk assessment model demonstrated that the single most important factor

related to the risk of illness caused by this organism is the level of V. parahaemolyticus in

oysters at the time of harvest. However, the model is based on a direct correlation between total

and pathogenic V. parahaemolyticus levels at time of harvest. We have also assumed that

pathogenic strains of V. parahaemolyticus grow at the same rate as non-pathogenic strains.

Consequently, as the level of total V. parahaemolyticus increases so does the number of

pathogenic V. parahaemolyticus. Accordingly, intervention measures aimed at controlling or

reducing the levels of V. parahaemolyticus in oysters should have a direct bearing on

controlling or reducing the risk associated with this pathogen. Water and air temperatures at

time of harvest were found to be the major factors influencing the initial levels of this pathogen

in oysters. Air temperature was also found to influence the growth of V. parahaemolyticus in

oysters after harvest and, thus, the levels in oysters at the time of consumption. In oysters left

unrefrigerated after harvest, V. parahaemolyticus rapidly multiply. The model demonstrated

that these factors could have a significant impact on the likelihood of illnesses occurring.

Model simulations of intervention measures indicated a significant reduction in the probability

of illness when the oysters are cooled immediately after harvest and kept refrigerated. During

refrigerated storage V. parahaemolyticus densities slowly but steadily decrease. Mild heat

treatment (5 min at 50 °C) of oysters, which causes at least a 4.5 log decrease in the number of

viable V. parahaemolyticus in oysters, practically eliminates the likelihood of illness occurring.

Quick-freezing and frozen storage of oysters, which causes a 1 to 2 log decrease in viable V.

parahaemolyticus oyster levels, also substantially reduces the probability of illness.
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Earlier human feeding trials conducted in Japan showed that illnesses occurred at levels of V.

parahaemolyticus, which were comparable to those of other bacterial strains when administered

with antacids, and that the number of illnesses increased with increasing levels of pathogenic V.

parahaemolyticus. Three different dose-response distribution models were considered for the

purpose of extrapolating from these data the risk of illness associated with lower levels of

exposure associated with consumption of raw oysters. Distributions of ingested dose were

developed by considering the probabilistic variation of number and meat weight of oysters in a

serving or eating occasion in addition to the expected variation of the density of pathogenic V.

parahaemolyticus determined in the Harvest and Post Harvest Modules.

On the basis of all available epidemiological data, and in the absence of quantitative data to the

contrary, equal virulence among pathogenic strains of V. parahaemolyticus was assumed for

modeling purposes. Pathogenic strains of V. parahaemolyticus were defined as those stains

possessing the ability to produce a thermostable direct hemolysin (TDH). The assessment of

dose-response was developed based on this definition. This may be modified as new data

become available that identify new virulence determinants.

Based on all available epidemiological information, it was assumed that all consumers are

equally susceptible to infection by V. parahaemolyticus. The probability of illness was

determined as an increasing function of ingested dose of pathogenic V. parahaemolyticus.

Epidemiological case series data clearly suggest a greater probability of an infection leading to

septicemia and death among immune compromised individuals. This is reflected in modelbased estimates of severe outcome (i.e. septicemia).

The outputs from this project provide estimates of risk for illness among consumers of raw

oysters (average nationwide yearly incidence of 4,750 cases per year, with a range from 1,000

to 16,000 cases - for the Gulf Coast, 25 (winter), 1,200 (spring), 3,000 (summer), and 400 (fall);

for the Pacific Northwest, 15 (spring) and 50 (summer); for the Mid-Atlantic, 10 (spring) and 12

(summer); and for the Northeast Atlantic, 12 (spring), 30 (summer) and 7 (fall)). Risks increase

with increasing levels of total V. parahaemolyticus and therefore pathogenic strains of V.

parahaemolyticus.

Simulations on the rate of illness caused by oyster-servings where the levels of V.

parahaemolyticus at harvest are at or above 10,000 cells/g, suggest that approximately 15% of

the illnesses are associated with the consumption of oysters containing greater than 10,000 V.

parahaemolyticus /g at time of harvest. Comparing the number of servings that cause illness to

those that do not, the simulations demonstrate that on average 0.6% of the servings result in

illness when V. parahaemolyticus levels are at 10,000 cells/g or above.

Data gaps relevant to the risk assessment have been identified, and further research is needed to

narrow risk estimates and reduce the uncertainties associated with these. For example, more

definitive information on other potential virulence factors, such as the capacity for invasion of

the enterocytes, production of an enterotoxin, and urease production would enable better

differentiation of pathogenic and non-pathogenic strains and, thereby, enable more conclusive

estimates on the prevalence of pathogenic strains in shellfish waters and oysters. Another

special interest for this risk assessment requiring further data is the prevalence and abundance of
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pathogenic V. parahaemolyticus serotype O3:K6 in oysters at harvest, and also at consumption.

In addition, though some studies have suggested that the immune and physiological status of an

oyster could be an important factor in the prevalence of total V. parahaemolyticus and,

therefore, in the prevalence of pathogenic V. parahaemolyticus, further data on this also is

needed. Research needs identified by this risk assessment are summarized for the following

areas:

• Environmental factors that influence distribution and abundance of pathogenic V.

parahaemolyticus in the environment for every region and season (i.e. temperature shifts,

salinity, animal passage, predation, and introduction of strains from distant areas).

• Rates of hydrographic flushing (water turnover) in shellfish harvest areas based on levels of

freshwater flows, tidal changes, winds, and depth of harvesting area.

• Distribution and abundance of pathogenic V. parahaemolyticus in oysters at harvest.

• Growth and survival of pathogenic V. parahaemolyticus in oysters at various temperatures.

• Industry post harvest handling practices (i.e. time to refrigeration, cooldown periods, and

length of refrigerated storage).

• Consumption patterns (frequency of raw oyster consumption from different harvest regions

or seasons, and consumption by at risk groups).

• Dose-response data: how many V. parahaemolyticus organisms are required to cause illness,

and severity of the illness.

• Potential virulence factors other than TDH (i.e. TRH, urease, enterotoxins, acid adaptation,

and invasion of intestinal cells).

• Development of assays to compare virulence potential among different strains

• Role of the oyster (physiology, immune status) in levels of V. parahaemolyticus.

• Consumer handling of oysters prior to consumption

• Improved global public health surveillance of V. parahaemolyticus to identify new epidemic

strains as they emerge.

This risk assessment significantly advances our ability to describe our current state of

knowledge about this important foodborne pathogen, while simultaneously providing a

framework for integrating and evaluating the impact of new scientific knowledge on enhancing

public health. 

The results of this draft risk assessment on V. parahaemolyticus are influenced by the

assumptions and data sets that were used to develop the exposure assessment and hazard

characterization. These results, particularly the predicted estimates of risk for illness among

consumers of raw oysters, and the most significant parameters, which influence the incidence of

illness, could change as a result of future data obtained from the Interim Control Plan and the

FDA actively seeking new information, scientific opinions, or data during the public comment

period. It is anticipated that periodic updates to the risk model will continue to reduce the

degree of uncertainty associated with risk estimates, and that this will assist in making the best

possible decisions, policies, and measures for reducing the risk posed by V. parahaemolyticus in

raw molluscan shellfish.
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TABLE OF CONTENTS

CONTRIBUTORS .......................................................................................i

ACKNOWLEDGMENTS..........................................................................ii

COMMENTS REQUESTED DURING PUBLIC COMMENT

PERIOD .......................................................................................................iii

EXECUTIVE SUMMARY........................................................................v

TABLE OF CONTENTS ..........................................................................ix

LIST OF TABLES .....................................................................................xi

LIST OF FIGURES ..................................................................................xii

GLOSSARY OF TERMS .......................................................................xiv

ACRONYMS AND ABBREVIATIONS...............................................xv

I. INTRODUCTION...................................................................................1

II. RISK ASSESSMENT PROCESS.......................................................4

III. HAZARD IDENTIFICATION..........................................................5

IV. EXPOSURE ASSESSMENT..............................................................6

Harvest Module ............................................................................................6

Routes of introduction of Vibrio parahaemolyticus into shellfish growing areas and in

shellfish....................................................................................................................................6

Prevalence and persistence of V. parahaemolyticus in shellfish and in shellfish growing

areas.........................................................................................................................................7

Modeling of the Harvest Module...........................................................................................9

Effect of water temperature and salinity on total V. parahaemolyticus densities........10

Water Temperature Distributions ...................................................................................17

Prediction of the distribution of pathogenic V. parahaemolyticus densities.................20

Post Harvest Module ..................................................................................24

Mitigation Strategies............................................................................................................24

Reducing time to refrigeration.........................................................................................24

Mild heat treatment...........................................................................................................25

Freezing treatment.............................................................................................................25

Depuration..........................................................................................................................25

Relaying ..............................................................................................................................25

Modeling of the Post Harvest Module ................................................................................26

Growth of V. parahaemolyticus from harvest to first refrigeration..............................27

Growth Rate Model...........................................................................................................27

Distribution of ambient air temperature .........................................................................29

Distribution of time oysters are left unrefrigerated.......................................................30

Excess growth of V. parahaemolyticus during cool down time ......................................33

Die-off of V. parahaemolyticus during cold storage ........................................................35
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Mitigation Strategies.........................................................................................................36

Public Health Module .................................................................................36

Epidemiology.........................................................................................................................37

Outbreaks...........................................................................................................................37

Case Reports ......................................................................................................................38

Case Series .........................................................................................................................38

Geographic distribution....................................................................................................41

Implicated Foods ...............................................................................................................41

Consumption.........................................................................................................................42

Frequency of Consumption and Amount of Raw Molluscan Shellfish Consumed.....42

Population at Risk.............................................................................................................42

Oyster Landings Data .......................................................................................................43

V. HAZARD CHARACTERIZATION/DOSE RESPONSE............44

Human Clinical Feeding Studies ................................................................45

Feeding trials with V. parahaemolyticus.............................................................................45

Feeding trials with non O1 V. cholerae...............................................................................45

Animal Models............................................................................................45

Animal models for V. parahaemolyticus.............................................................................46

Animal models for other Vibrio spp....................................................................................46

Factors Influencing the Infectious Dose of V. parahaemolyticus.................46

Modeling of the Public Health Module .......................................................47

Distribution of dose of pathogenic V. parahaemolyticus per eating occasion..................48

Number of raw oyster servings (eating occasions)............................................................50

Dose-Response ......................................................................................................................51

Severity of Illness..................................................................................................................55

VI. RISK CHARACTERIZATION.......................................................59

Simulation Results......................................................................................59

Probable distribution of illness associated with regional/seasonal oyster harvest.........60

Predicted effect of mitigation strategies on risk/probability of illness............................66

Evaluation of the fda guideline of 10,000 V. parahaemolyticus/g of seafood...................69

Sensitivity Analysis.....................................................................................71

Model Validation........................................................................................74

The value of information which could be obtained by additional studies...77

Comments on the model .............................................................................78

VII. DISCUSSION ....................................................................................81

Data Gaps and Future Research Needs......................................................87

VIII. REFERENCES................................................................................90

IX. APPENDICES.................................................................................. 100

Appendix I................................................................................................ 100

Chronology of Technical and Scientific Reviews of the FDA Vibrio parahaemolyticus

Risk Assessment Document ...............................................................................................100

Appendix II............................................................................................... 102

Model for the FDA Vibrio parahaemolyticus Risk Assessment.......................................102
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LIST OF TABLES

Table IV-1. Summary statistics (mean, variance and correlation) of the year-to-year

variation in the mean and standard deviation of noontime water temperature

distributions for different regions and seasons................................................................18

Table IV-2. Estimates of pathogenic V. parahaemolyticus (Vp) as a percentage of total V.

parahaemolyticus.................................................................................................................21

Table IV-3. Means and standard deviations of the distribution of the difference between

recorded air and water temperatures at midday (° C)....................................................30

Table IV-4. Minimum, maximum and mean duration of oyster harvest (length of

harvesting operation) for different regions and seasons.................................................31

Table IV-5. Discrete approximation of variation in the growth rate of V. parahaemolyticus

during a cooldown period of k hours................................................................................34

Table IV-6. Summary statistics of the distribution of storage times (time under

refrigeration in days) of oysters samples obtained during the ISSC/FDA retail study

..............................................................................................................................................35

Table IV-7. Clinical symptoms associated with gastroenteritis caused by V.

parahaemolyticus.................................................................................................................37

Table IV-8. Clinical syndromes of raw oyster-associated Vibrio infections in Florida, 1981-

1994......................................................................................................................................39

Table V-1. National Marine Fisheries Service (NMFS) average yearly oyster landings

1990-1998.............................................................................................................................50

Table V-2. Dose-response models of the relationship between probability of illness and

number of V. parahaemolyticus organisms ingested........................................................52

Table VI-1. Effect of selected uncertainty and variability parameters with respect to Gulf

Coast summer harvest: average and relative variation of predicted risk per serving

for the V. parahaemolyticus risk assessment model.........................................................78
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LIST OF FIGURES

Figure IV-1. Schematic depiction of the Harvest Module of the V. parahaemolyticus (Vp)

risk assessment model. .........................................................................................................9

Figure IV-2. Observed log10 V. parahaemolyticus (Vp) densities in oysters versus water

temperature at different salinities.....................................................................................13

Figure IV-3. Observed log10 V. parahaemolyticus (Vp) densities in oysters versus salinity at

different temperatures.......................................................................................................14

Figure IV-4. Effect of salinity on predicted mean log10 V. parahaemolyticus (Vp) density in

oysters and water relative to predicted density at optimal salinity (22 ppt).................15

Figure IV-5. Observed log10 V. parahaemolyticus (Vp) densities in oysters versus water

temperature at different salinities.....................................................................................16

Figure IV-6. Schematic depiction of the Post Harvest Module of the V. parahaemolyticus

(Vp) risk assessment model................................................................................................26

Figure IV-7. Predicted loglinear growth of V. parahaemolyticus (Vp) from initial density of

1,000 (3 log10) Vp/g as a function of ambient air temperature.......................................29

Figure IV-8. Beta-PERT probability density distribution for the duration of harvesting

operations during the winter season (Mid-Atlantic, Northeast Atlantic, Gulf Coast,

excluding Louisiana) (44)...................................................................................................32

Figure V-1. Schematic depiction of the Public Health Module of the V. parahaemolyticus

(Vp) risk assessment model................................................................................................47

Figure V-2. Observed frequency of number of oysters consumed per serving (University of

Florida consumption survey) (36). ....................................................................................49

Figure V-3. Maximum likelihood estimates (MLE) of the Beta-Poisson, Gompertz, and

Probit dose-response curves based on pooled data from human feeding studies of V.

parahaemolyticus (Vp)........................................................................................................54

Figure V-4. Uncertainty distribution of infectious dose of V. parahaemolyticus

corresponding to 10-3 risk for Beta-Poisson, Gompertz, and Probit dose-response

models..................................................................................................................................55

Figure VI-1. Schematic diagram of the V. parahaemolyticus (Vp) risk assessment model

showing integration of all the modules.............................................................................60

Figure VI-2. Effect of structural uncertainty of dose-response on projected number of

illnesses associated with V. parahaemolyticus (Vp) consumption in the Louisiana Gulf

Coast, summer harvest). ....................................................................................................61

Figure VI-3. Probable number of V. parahaemolyticus (Vp) illnesses associated with spring

and summer Gulf Coast harvests......................................................................................63

Figure VI-4. Probable number of V. parahaemolyticus (Vp) illnesses associated with fall

and winter Gulf Coast harvests.........................................................................................64

Figure VI-5. Probable number of V. parahaemolyticus (Vp) illnesses associated with

spring and summer Pacific Coast harvest........................................................................64

Figure VI-6. Probable number of V. parahaemolyticus (Vp) illnesses associated with

spring and summer Mid-Atlantic harvest........................................................................65

Figure VI-7. Distribution of probable number of cases of V. parahaemolyticus-associated

cases of septicemia occurring per year (all seasons and regions)...................................66
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Figure VI-8. Effect of potential mitigations on the distribution of probable number of

illnesses associated with V. parahaemolyticus (Vp) in oysters harvested from the Gulf

Coast in the summer ..........................................................................................................67

Figure VI-9. Effect of potential mitigations on the distribution mean risk of V.

parahaemolyticus illnesses per serving associated with Gulf Coast harvest..................68

Figure VI-10. Effect of potential mitigations on the distribution mean risk of V.

parahaemolyticus illnesses per serving associated with Pacific Coast harvest..............68

Figure VI-11. Effect of potential mitigations on the distribution mean risk of V.

parahaemolyticus illnesses per serving associated with Mid-Atlantic Coast harvest:..69

Figure VI-12. Potential effect of control of total V. parahaemolyticus per gram at harvest

(Louisiana Gulf Coast summer harvest). .........................................................................70

Figure VI-13. Tornado plot of influential parameters on log10 risk of V. parahaemolyticus

(Vp) illness per serving of raw oysters (Gulf Coast excluding Louisiana, summer

oyster harvest).....................................................................................................................72

Figure VI-14. Tornado plot of influential parameters on log10 risk of V. parahaemolyticus

(Vp) illness per serving of raw oysters (Louisiana Gulf Coast summer harvest).........72

Figure VI-15. Tornado plot of influential parameters on log10 risk of V. parahaemolyticus

(Vp) illness per serving of raw oysters (Pacific Northwest Coast summer harvest)....73

Figure VI-16. Tornado plot of influential parameters on log10 risk of V. parahaemolyticus

(Vp) illness per serving (Mid-Atlantic summer harvest)................................................73

Figure VI-17. Observed retail level distribution of density of total V. parahaemolyticus

(Vp) compared to model predictions for all seasons (Gulf harvest) (28).......................76

Figure VI-18. Observed retail level distribution of density of total V. parahaemolyticus

(Vp) compared to model predictions for all seasons (Mid-Atlantic harvest) (28)........76

Figure VI-19. Observed retail level distribution of density of total V. parahaemolyticus

(Vp) compared to model predictions for all seasons (Pacific harvest) (44)...................77
GLOSSARY OF TERMS

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GLOSSARY OF TERMS

Definitions

Case Series - study of sporadic cases of similar illness occurring over a period of time.

Depuration - the process of reducing pathogenic organisms that may be present in shellfish

using a controlled aquatic environment, such as land-based tanks, as the treatment

process.

Dose - the number of pathogenic V. parahaemolyticus consumed in oysters at one sitting.

Dose-response - the relationship of the levels of V. parahaemolyticus ingested with the

frequency and magnitude of illness.

Gastroenteritis – inflammation of the gastrointestinal tract; symptoms include diarrhea,

vomiting, or abdominal cramps, caused by an infecting organism which is present in feces.

Raw molluscan shellfish - raw (uncooked) oysters, clams, and mussels.

Outbreak - the occurrence of similar illness involving 2 or more persons not from the same

household resulting from the ingestion of a common food.

Relaying - the process of reducing pathogenic organisms or deleterious substances that may

be present in shellfish using the ambient environment as the treatment process, by

transferring shellfish from a growing area classified as restricted or conditionally

restricted to a growing area classified as approved or conditionally approved.

Sensitive subpopulation - group of people with greater vulnerability to more severe Vibrio

parahaemolyticus disease (i.e., septicemia) as a result of some underlying state of

compromised health, such as liver disease, blood disorder, or immunodeficiency.

Septicemia - a systemic disease associated with the presence and persistence of pathogenic

microorganisms or their toxins in the blood.

Thermostable direct hemolysin - a toxin produced by Vibrio parahaemolyticus that lyses red

blood cells in Wagatsuma agar .

Thermostable-related hemolysin - a toxin very similar in action and characteristics to, but

genetically distinct from the thermostable direct hemolysin
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ACRONYMS AND ABBREVIATIONS

CFSAN - Center for Food Safety and Applied Nutrition

GCSL - FDA Gulf Coast Seafood Laboratory, Dauphin Island

ICP - ISSC/FDA Interim Control Plan for monitoring levels of pathogenic V.

parahaemolyticus in oysters at time of harvest

ISSC - Interstate Shellfish Sanitation Conference

MSI - Molluscan Shellfish Industry

NACMCF - National Advisory Committee for the Microbiological Criteria for Foods

NERR - National Estuarine Reserve Sites program

NBDC - National Buoy Data Center

NMFS- National Marine Fisheries Services

NOAA - National Oceanic and Atmospheric Administration

NOS - National Ocean Services

NSSP - National Shellfish Sanitation Program for control of Vibrio vulnificus

PCSGA - Pacific Coast Shellfish Growers Association

RAC - Risk Assessment Consortium

STORET - Storage and Retrieval of U.S. Waterways Parametric Data database

g - grams

HGMF procedure - Hydrophobic Grid Membrane Filtration procedure

h - hours

KP+ - Kanagawa-positive

min - minute

ml – milliliters

MLE - Maximum likelihood estimates

MPN - most probable number

/g - per gram

ppt - parts per thousand

TDH - thermostable direct hemolysin

TRH - thermostable-related hemolysin

Vp - V. parahaemolyticus

Vppath - pathogenic strains of V. parahaemolyticus
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I. INTRODUCTION

In response to four separate outbreaks that occurred in 1997 and 1998 in the United States (21,

22), FDA conducted a risk assessment (RA) on the public health impact of Vibrio

parahaemolyticus transmitted by raw oysters. Initiated in January 1999, this risk assessment

focused specifically on oysters, since this was the food predominantly linked to the outbreaks.

In May 1999, FDA announced its intent to conduct a risk assessment of the public health impact

of V. parahaemolyticus in raw molluscan shellfish in the Federal Register (43). At that time,

the public was invited to comment on the planned assessment and submit scientific data and

information for use in the assessment. The advice and recommendations of the National

Advisory Committee on Microbiological Criteria for Foods (NACMCF) were sought in May

and September of 1999, on the assumptions and the risk assessment model structure to be used.

During the conduct of this risk assessment, FDA solicited the technical advice and opinions of

several scientific and state shellfish experts. In addition, critical review of this risk assessment

model was solicited and received in December 1999 and May 2000, from members of the

Interagency Risk Assessment Consortium, other government employees, and special

government experts (SGE).

A chronology of the technical and scientific review involved in the development of this risk

assessment is provided in Appendix I.

Over 700 cases of illness caused by V. parahaemolyticus (21, 22) resulted from the 1997 and

1998 outbreaks that occurred in three regions of the country, one in the Gulf Coast, two in the

Pacific Northwest, and one in the Northeast. One particular serotype (O3:K6) of V.

parahaemolyticus, previously associated only with illnesses in Asia (109), was predominantly

isolated from patients as the principal cause of the outbreaks occurring in the Northeast and

Gulf Coast regions. It has been suggested that the 1998 Gulf Coast outbreak may have been a

result of elevated water temperatures (34). The influences exerted by these and other factors

remain uncertain. Whatever factors were involved in these four incidents, the recurrence of V.

parahaemolyticus outbreaks in North America and the large number of individuals affected

have renewed concerns for this pathogen in raw oysters as a microbial food safety problem in

the United States.

First isolated and implicated in an outbreak of food poisoning in Japan in 1950 (48), V.

parahaemolyticus has been associated with outbreaks and individual cases of illness in the

United States since 1969 (11, 32, 91, 107). However, since 1981 and until 1997 the only V.

parahaemolyticus illnesses transmitted by raw molluscan shellfish in the United States have

been reports of intermittent, sporadic cases, and relatively few of these occurred. The

recurrence of outbreaks caused by this organism was unexpected, and the wide-ranging

geography and total number of illnesses recorded were surprising. Consequently, a systematic

evaluation of factors affecting V. parahaemolyticus in oysters and the sequence of events
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leading to consumer illnesses was undertaken in order to gain a fuller understanding of the risks

posed by this bacterial pathogen.

The levels of V. parahaemolyticus in oysters at the time of consumption can be influenced

substantially by the methods chosen for harvesting and handling oysters after harvest, and these

practices may vary considerably in different geographic areas and at different times of year. For

example, the period of unrefrigerated storage may vary from just a few hours to more than a

day, and perhaps longer in some cases. In general, the longer oysters remain unrefrigerated, the

higher the level of V. parahaemolyticus in those oysters will become, and the higher the

environmental temperature, the faster V. parahaemolyticus will grow. Thus, the levels of V.

parahaemolyticus in oysters kept alive until consumption can range widely. This seemed likely

to be an important element in estimating the level of risk to consumers.

The infectious dose of V. parahaemolyticus is not known. Based on data from volunteer studies

conducted more than 25 years ago, along with data from earlier United Staets outbreaks caused

predominantly by cross-contamination of cooked crabs, FDA had previously indicated that V.

parahaemolyticus in shellfish should not exceed a level of 10,000 viable cells per gram (64).

However, data obtained during the recent outbreaks indicated that fewer than 10,000 V.

parahaemolyticus per gram were present in oysters obtained from implicated harvest areas

during the same period (21). In fact, the overall levels of V. parahaemolyticus found in some

oysters from implicated harvest sites were as low as 100 and often less than 1,000 cells per

gram. In view of this, and the fact that not all V. parahaemolyticus strains are pathogenic, it

seemed possible that the earlier guidance alone may not be sufficient to protect consumers from

V. parahaemolyticus illness associated with raw oysters.

The risk assessment has focused on raw oysters because these were the predominant seafood

implicated in the 1997 and 1998 outbreaks (5, 21, 22). This endeavor investigated the

prevalence of V. parahaemolyticus, the extent of consumer exposure to the organism, the

resulting public health impact, and the effects of several post harvest treatments to control

exposure to this hazard in raw oysters. A systematic evaluation of the available scientific

information was conducted to assist public health officials in assessing different potential

control measures and developing food safety guidance and policies. The risk assessment had

two main objectives: (1) create a mathematical model and assess the current risk of becoming ill

due to the consumption of pathogenic V. parahaemolyticus in raw oysters; and (2) develop a

comprehensive and current scientific framework, which will assist the agency with the review

of current programs relating to the regulation of V. parahaemolyticus in raw molluscan shellfish

to ensure that such programs protect the public health.

Exposure is a function of the prevalence of V. parahaemolyticus in the oysters consumed and

the consumption patterns of the population. Thus, the number of pathogenic V.

parahaemolyticus in oysters at consumption, and what leads to this number, is critical exposure

information. Accordingly, the scope of the risk assessment specifically attempted to address the

following questions:

• What is the frequency of occurrence of pathogenic strains of V. parahaemolyticus in the

shellfish waters, and what parameters (e.g., water temperature, salinity, turbidity, and
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nutrient profiles) can be used as indicators of the presence of the organism in growing

waters?

• What is the frequency of occurrence of pathogenic strains of V. parahaemolyticus in

oysters, and what are the numbers of viable pathogenic organisms at time of

consumption? How are levels present in the bivalves at the time of consumption related

to the initial levels in the growing waters?

• What is known about the dose-response relationship from outbreak, epidemiological,

animal and other studies? What are the differences in dose-response relations among

different strains and serotypes of V. parahaemolyticus, and among consumers with

different susceptibilities?

• What is the role of post harvest handling that may be influencing the numbers of V.

parahaemolyticus in oysters? What reductions in risks can be achieved by intervention

strategies such as depuration, (the process by which shellfish are cleansed in seawater in

tanks), or relaying (the process by which shellfish are cleansed by transferring them to

clean shellfish growing areas).

• What is the adequacy of current scientific knowledge and where should future research

be focused to reduce the uncertainty in the risk estimate?

Thus, the risk assessment encompassed the relationships between oysters, V. parahaemolyticus,

and illnesses, and exposure to pathogenic V. parahaemolyticus. The risk assessment attempted

to evaluate the following factors:

• Evidence for increased risks from certain newly emerging strains causing outbreaks,

such as serotype O3:K6.

• The effectiveness of potential strategies for limiting exposure of the public to raw

oysters containing pathogenic V. parahaemolyticus and reductions in risks, which might

be achieved by intervention strategies such as rapid cooling, quick freezing, mild heat

treatment, depuration and relaying.

• Current criteria for opening and closing shellfish harvest waters.

• Current FDA guidance that shellfish should contain less than 10,000 V.

parahaemolyticus per gram.
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II. RISK ASSESSMENT PROCESS

Risk assessment provides the scientific basis for risk analysis. It is the process of determining

the likelihood that exposure to a hazard, such as a foodborne pathogen, will result in harm or

disease; it helps characterize the nature and magnitude of risks. Risk assessments also assist

regulators in decision making on food safety guidance and policies by providing a systematic

evaluation of the state of knowledge related to the hazard including the degree of uncertainty.

The V. parahaemolyticus risk assessment (VPRA) process illustrated in this document adhered

to the framework proposed by the CODEX Committee on Food Hygiene (68) and by the

National Advisory Committee on Microbiological Criteria for Foods (103), which involves four

steps: Hazard Identification, Exposure Assessment, Hazard Characterization/Dose-Response,

and Risk Characterization.

This risk assessment utilized quantitative risk assessment modeling, which is a mathematical

process used to evaluate the likelihood of adverse human health effects occurring following

exposure to a pathogenic microorganism. Or more simply stated, it describes what we know

and how certain we are of what we know. The risk is expressed as a mathematical statement of

the chance of illness or death after exposure to a specific pathogen and it represents the

cumulative probabilities of certain events happening and the uncertainty associated with those

events. Quantitative risk assessment modeling is a relatively new approach to the field of

microbial risk. The data are represented as large sets of numbers called distributions rather than

as point estimates, which offer several potential advantages over traditional approaches. One

advantage is that distributions may represent the spread of real world data more accurately than

point estimates like a mean. Distributions can also reflect the presence of uncertainty in the

data. Another potential advantage is that modeling allows risk assessors to test which factors

are most important in determining the magnitude of a risk or what effect control measures will

have on a risk. Quantitative models are also flexible; inputs and model components can be

changed readily as new data become available.

Determinants of the hazard of V. parahaemolyticus illness due to consumption of oysters

containing this pathogen, include: the initial levels of pathogenic V. parahaemolyticus in the

oysters at harvest, the effect of normal handling and processing on these levels, and the

pathogen’s capability to multiply and reach an infectious level in the food prior to consumption.

Taking into account these determinants, the risk assessment was divided into three different

modules within the CODEX framework: Harvest, Post Harvest, and Public Health Modules.

The Public Health Module was further subdivided into three segments: epidemiology,

consumption, and dose-response, even though dose-response forms the Hazard Characterization

section of the risk assessment.
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III. HAZARD IDENTIFICATION

Hazard identification is the identification of biological, chemical, or physical agents capable of

causing adverse health effects that may be present in a particular food or group of foods. V.

parahaemolyticus is a Gram-negative, halophilic bacterium that occurs naturally in estuaries

and is recognized as an important bacterial seafood-borne pathogen throughout the world. The

organism can cause an acute gastroenteritis and, on rare occasions, septicemia. The minimum

infectious dose is not known. It is normally present in many seafoods, including fish,

crustaceans, and molluscan shellfish. However, not all strains of V. parahaemolyticus cause

illness and, in fact, pathogenic strains very rarely have been isolated from the environment or

seafood. Apparently non-pathogenic V. parahaemolyticus are far more prevalent in nature.

Several different virulence traits have been associated with the pathogenesis of V.

parahaemolyticus strains. These include their ability to: a) produce a thermostable direct

hemolysin (TDH) (96), b) to invade the enterocytes (3) and c) to produce an enterotoxin (61).

However, these last two characteristics are not normally investigated in the environmental or

clinical isolates, and the only trait known to reliably distinguish pathogenic from nonpathogenic V. parahaemolyticus is the production of a thermostable direct hemolysin (TDH).

Pathogenic strains possess a tdh gene and produce TDH, and non-pathogenic strains lack the

gene and the trait (96). Non-pathogenic strains, as identified by the absence of the TDH, are

predominantly found in the environment.

The most common clinical manifestation of V. parahaemolyticus is gastroenteritis, which is

usually a self-limited illness with moderate severity and short duration (11, 12, 57). However,

on rare occasions, infection can result in septicemia that can be life threatening (57, 83).

Gastroenteritis, due to a specific organism, is an inflammation of the gastrointestinal tract,

characterized by diarrhea, vomiting, or abdominal cramps, and that organism is isolated from a

stool sample. Septicemia is a systemic disease, characterized by fever or hypotension, and the

organism is isolated from the blood. Patients with septicemia often have underlying medical

conditions (83).
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IV. EXPOSURE ASSESSMENT

Exposure assessment is the determination of the likelihood of ingesting pathogenic V.

parahaemolyticus by eating raw molluscan shellfish harboring the organism and the amount of

pathogenic V. parahaemolyticus present when consumed. Exposure assessment is subdivided

into Harvest, Post Harvest, and the epidemiology and consumption segments of the Public

Health Module.

Harvest Module

The Harvest Module identifies the parameters contributing to the likelihood that shellfish in a

growing area will contain disease-causing (pathogenic) strains of V. parahaemolyticus and the

levels in which they’re found. These parameters are listed below.

Routes of introduction of V. parahaemolyticus into shellfish growing areas and in

shellfish

Vibrio spp. are found in the estuarine environment in the tropical to temperate zones. Several

studies have been published on the concentration of V. parahaemolyticus in shellfish growing

areas (35, 38, 70, 73, 74, 76, 77, 89). There are several pathways by which V. parahaemolyticus

strains may be introduced into shellfish growing areas. V. parahaemolyticus may originate or

new strains may be introduced naturally by terrestrial and aquatic animals, or through human

activities such as “relaying” shellfish or releasing ballast water. Terrestrial and aquatic animals

(including plankton, birds, fish, reptiles) may harbor virulent strains of V. parahaemolyticus and

may play a role as intermediate hosts and vehicles for spread (118). V. parahaemolyticus has

been isolated from a number of fish species where it is associated primarily with the intestinal

contents(101). V. parahaemolyticus can be introduced into non-contaminated areas by relaying

shellfish prior to commercial harvesting.

Ship ballast release may be a potential mechanism of introducing V. parahaemolyticus into a

particular environment. Most cargo ships must carry substantial quantities (millions of gallons)

of ballast water to operate safely when they are not carrying cargo. Cargo ships take on ballast

water from the body of water in which the ship originates. Having taken water on board, it is

normally retained until the ship is about to load cargo, at which point ballast water is

discharged. During de-ballasting, organisms picked up from one port could be introduced into

the loading port. Ship ballast may have spread the epidemic strain of V. cholerae to the U.S.

Gulf of Mexico (93). Strains of V. cholerae indistinguishable from the Latin American

epidemic strain were found in non-potable water taken from a cargo ship docked in the Gulf of

Mexico. The same could occur for V. parahaemolyticus.

Sewage discharge may indirectly influence the densities of V. parahaemolyticus present in

shellfish growing areas (141). For example, densities of V. parahaemolyticus in the water

column in Narragansett Bay, Rhode Island were correlated with fecal coliforms from sewage;

however, the effect of sewage was an indirect one mediated by stimulation of zooplankton with
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which the V. parahaemolyticus were associated. Laboratory studies showed that nutrients in the

sewage did not directly increase V. parahaemolyticus levels (141). Other reports have shown

that organic matter does have an effect on growth and survival of the organism (121). In

another study, the distribution of V. parahaemolyticus in sediments in Boston Harbor was

independent of densities of fecal coliforms (120).

Prevalence and persistence of V. parahaemolyticus in shellfish and in shellfish growing

areas

Once introduced, a number of factors are relevant to whether V. parahaemolyticus will become

established. These include interactions of environmental conditions, species and physiology of

the shellfish, and the genetics of the microorganism. Certain areas may have more favorable

environmental conditions that support establishment, survival, and growth of the organism.

Predictive factors to be considered in determining the prevalence of V. parahaemolyticus

include temperature (including El Niño and La Niña weather patterns), salinity, zooplankton,

tidal flushing (including low tide exposure of shellfish) and dissolved oxygen (4, 49, 70, 137).

Warmer temperatures and moderate salinities, especially those prevailing during the summer

months, favor the growth and survival of V. parahaemolyticus (31, 65, 101, 147). Most of the

shellfish-borne illnesses caused by this organism also occur in the warmer months. The Centers

for Disease Control and Prevention (CDC) randomly selected seven of the 76 (nine percent)

existing Texas Department of Health monitoring sites for environmental conditions in

Galveston Bay, and compared water temperature and salinity levels before and during the 1998

outbreak, with environmental data recorded over the previous five years (34). They

demonstrated a significant difference in mean values. During May 1998, water temperatures

were 81o Fahrenheit (F) compared with 76o F for the previous five years. In June, water

temperatures were 85o F, compared with 83o F for the previous five years. Significantly less

rainfall than usual, during April (0.59 inches) and May (0.02 inches) preceding the outbreak,

causing extreme drought conditions in Texas, resulted in markedly increased salinity levels in

Galveston Bay. During May, salinity levels were 18.3 parts per thousand (ppt) compared with

8.4 ppt for the previous five years. During June, salinity levels were 21 ppt compared with 9.1

ppt for the previous five years. It is therefore possible that environmental factors such as

increased temperature and salinity levels, known to promote growth of V. parahaemolyticus,

may have contributed to this outbreak. Elevated temperatures were also suspected to have

played a role in the 1997 outbreak on the West Coast (22).

Another variable that must be considered is that V. parahaemolyticus often “over-winters”

(survives the winter) in the sediment and is absent from the water column and oysters during the

winter months (69, 75, 136). During the summer, shellfish often have levels of V.

parahaemolyticus from 10- to 100-fold greater than those in the water (38, 73); therefore,

sediment should be the preferred samples for monitoring during the winter and shellfish should

be the preferred samples for monitoring during the summer. Under extreme environmental

conditions, Vibrio species, including V. parahaemolyticus, may enter a “viable but nonculturable (VBNC) phase” in marine waters and could be missed by traditional cultural methods

(15, 24, 110, 146). This issue remains a controversial one. Methods such as gene probes
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developed by the FDA are capable of detecting most virulent strains and are useful in

monitoring programs (51).

V. parahaemolyticus favors the presence of particulates, zooplankton and other chitin sources

(70, 102, 137). Microorganisms are incorporated into shellfish by filter feeding. Factors that

favor active filter feeding by shellfish increase the probability that shellfish in a given area will

take up the pathogen (100). Shellfish species and physiology (e.g., sexual maturity, immune

function, metabolic state) can affect survival and growth of disease-causing Vibrio spp. within

shellfish. There is evidence that the immune status of the shellfish may play an important role

in the prevalence and persistence of the microorganism (45, 85, 86, 111, 138). There also

appear to be seasonal differences in the oyster's cellular defense system. A recent study showed

that the bactericidal activity of hemocytes (oyster blood cells) was greater in summer than in

winter (50). Certain factors, such as the oyster parasite Perkinsus marinus, play a role in the

affinity of bacteria for shellfish tissue and the ability of oyster hemocytes to kill the internalized

organisms (85, 86, 126). Factors, such as spawning or adverse environmental conditions (e.g.,

the presence of chemicals in the environment: tributyltin oxide, polycyclic aromatic

hydrocarbons, wood preservative leachates), that reduce or stop filter feeding in shellfish, or

cause selective feeding (e.g., new nutrient sources) may prevent or delay incorporation of V.

parahaemolyticus into shellfish by affecting oyster physiology and possibly affect oysterbacterial interactions (124, 142, 143).

Persistence of virulent strains of V. parahaemolyticus in shellfish in the environment may be

dependent on several parameters. Whether virulent and non-virulent strains are affected in a

similar fashion by environmental and other factors is unknown. The presence of the urease

gene may provide a competitive environmental advantage over other strains allowing access to a

wider range of nutrients (1). Urease-positive strains have been identified as a predominant

cause of Vibrio-associated gastroenteritis on the U.S. West Coast and Mexico (1). The presence

of a pathogenicity island (a physical grouping of virulence-related genes) in V.

parahaemolyticus may foster rapid microevolution, promote growth and survival, and result in

transmission of factors, such as those responsible for virulence, to other strains (horizontal gene

transfer) (47, 62, 63). In addition, bacteriophages may genetically alter vibrios (14, 62).

The distribution and variation in levels of virulent V. parahaemolyticus in shellfish and among

shellfish growing areas may need to be determined before harvest because many of the

described factors may have contributed to higher concentrations in certain areas. During the

1998 outbreaks, the Pacific Northwest shellfish harvested from the Hood Canal area of

Washington were responsible for 32 of 48 (67 %) of the illnesses in the State of Washington

(132). In the Gulf Coast, 20 of 30 harvest sites were implicated. In the Atlantic Northeast

region, Oyster Bay Harbor (Area 47) was the only area implicated in the outbreak of that region

(21).
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Modeling of the Harvest Module

Although a number of factors have been identified as potentially affecting the levels of

pathogenic V. parahaemolyticus in oysters at time of harvest, there are not sufficient

quantitative data available to incorporate all of these factors into a predictive model. To

incorporate an environmental factor into the simulation, as a predictor of V. parahaemolyticus

densities at harvest, it is necessary to identify both the relationship of V. parahaemolyticus

densities to the parameter of interest and the regional and temporal variation of the parameter

within the environment. Moreover, due to the relatively low prevalence of pathogenic V.

parahaemolyticus and limitations of current methods of detection, the distribution of pathogenic

V. parahaemolyticus is not well understood. A critical issue in the development of the Harvest

Module simulation is the use of the estimated distribution of total V. parahaemolyticus densities

to bridge this data gap and derive an estimate of the distribution of pathogenic V.

parahaemolyticus densities in oysters at harvest. Figure IV-1 is a schematic depiction of the

parameters considered in modeling the Harvest Module. Preliminary modeling demonstrated

that the parameter, water salinity is not as strong a determinant of V. parahaemolyticus levels as

water temperature, and therefore is represented as a dotted bubble.

Figure IV-1. Schematic depiction of the Harvest Module of the V. parahaemolyticus (Vp)

risk assessment model.

Water temperature

total Vp/g

Water salinity

pathogenic Vp/g

Regional, seasonal & yearly variation
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Effect of water temperature and salinity on total V. parahaemolyticus densities

The best available data on the relationship of total V. parahaemolyticus densities in oysters (and

water) versus water temperature and salinity is found in the study by DePaola et al, 1990 (38).

This study was conducted throughout an entire year with collection of samples from all four

regions of the country (i.e., Northeast, Gulf Coast, Mid-Atlantic, and Pacific Northwest). A

total of 65 paired samples of oyster and water were analyzed for total V. parahaemolyticus by a

membrane filtration method. While there have been several other surveys of V.

parahaemolyticus between 1982 to 1995, these studies are typically limited to specific regions

and/or seasons , and few have reported quantitative data. These studies are summarized below.

Kelly and Stroh (78) examined V. parahaemolyticus frequency in natural and cultivated oysters

from British Columbia and isolated V. parahaemolyticus from 44% of natural and 21% of

cultivated oysters under warm conditions (July and August) but did not find V.

parahaemolyticus in March and April.

Kelly and Stroh (79) also reported an association with V. parahaemolyticus illness and V.

parahaemolyticus density in the estuarine waters of British Columbia. V. parahaemolyticus was

isolated in 11-33% of water samples collected during the summer with peak densities of 70

cfu/ml. Oysters were not examined.

Kaysner et al. (74) sampled water, sediment and oysters of Willapa Bay, WA during August

when salinity ranged from 23.6-30.5 ppt and temperature 15.5-22.6°C. Highest densities (log10

MPN/g) were found in sediments (1.6-5.4), followed by oysters (1.5-4.0) and water (0.5-3.0); a

similar trend was observed with frequency of isolation.

Tepedino (130) surveyed Long Island oysters from October to June and found 33% to contain

V. parahaemolyticus with an MPN range of 3.6-23/g.

Hariharan et al. (55) conducted a year long survey of Prince Edward Island, Canada mussels

and oysters and V. parahaemolyticus was isolated from 4.7% and 6.7%, respectively.

Chan et al. (23) examined V. parahaemolyticus levels in seafood from Hong Kong from June

through October. Mean V. parahaemolyticus densities in oysters (harvest), mussels (market)

and clams (market) were 3.4 x 104, 4.6 x 104, and 6.5 x 103 per gram, respectively.

Kiiyukia et al. (81) enumerated V. parahaemolyticus in water and sediments of Japan. They

isolated V. parahaemolyticus in 2/8 market oyster samples but did not enumerate V.

parahaemolyticus in oysters.

Ogawa et al. (108) investigated the ecology of V. parahaemolyticus in Hiroshima Bay from July

1987 through June 1988. The highest incidence of detectable V. parahaemolyticus (68.8%) was

found from May to October when water temperature ranged from 19.3 to 22.0°C. V.

parahaemolyticus levels in oysters were seasonal and ranged from 103 – 101/100g (108). This

study also compared favorably with the DePaola et al. 1990 study (38).
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DePaola et al. (40) had previously evaluated 4 methods for enumeration of V. parahaemolyticus

in natural seawater and oysters and found considerable variability between methods for V.

parahaemolyticus recoveries; highest recoveries were obtained with a method using filtration

through a hydrophobic grid membrane.

DePaola et al. in 1990 (38) enumerated V. parahaemolyticus (hydrophobic grid method) in

seawater and oysters samples collected seasonally from May 1984 through April 1985 from

shellfish growing areas from the Pacific, Gulf and Atlantic Coasts. Seasonal and geographical

distributions of V. parahaemolyticus were related to water temperature, with highest densities in

samples collected in the spring and summer from the Gulf Coast.

We considered the study by DePaola et al. (38) to be most appropriate for the purpose of

quantitative risk assessment of V. parahaemolyticus illness from consumption of U.S. oysters.

This study, which is the most comprehensive regional/seasonal study available, examined

seasonal changes in V. parahaemolyticus density in oysters from major oyster producing areas

representative of the Pacific, Gulf and Atlantic Coasts (38). Studies reporting only presence or

absence of detectable V. parahaemolyticus are of limited value for quantitative risk assessment

(55, 78, 79, 81). Of the additional studies available reporting quantified V. parahaemolyticus

densities in oysters, samples were either obtained from a single estuary (23, 55, 74, 78, 79, 81,

130), were not seasonal (23, 74, 130), or did not report salinity and temperature (23, 130).

Differences in methodology used by the various investigators may also have affected V.

parahaemolyticus recoveries and complicate comparisons between studies. V.

parahaemolyticus levels observed in oysters from Long Island, NY (130) were similar to those

reported by DePaola et al. (38) from the Northern Atlantic Coast during the fall, winter and

spring. Kaysner et al. (74) observed higher V. parahaemolyticus densities in Willapa Bay, WA

in August than reported on the Pacific Coast during the summer by DePaola et al. (38). This

difference may have been due to the small number of samples (N=4) collected from the Pacific

Coast during the summer by DePaola et al. (38), or to favorable environmental conditions for V.

parahaemolyticus abundance in Willapa Bay during the study by Kaysner et al. (74). Including

the data from other studies of the Atlantic (130) and Pacific Coasts (74) would increase the

sample size. However, these studies employed different methodology than that used by

DePaola et al. (38) and inclusion of these data could bias comparisons between other seasons or

regions that did not include data from these individual estuaries using different methods.

Specifically, due to differences in method error associated with various analytical methods,

statistical analysis of pooled data must account for the differences in variation of observed

measurements according to the analytical methods used. Although this may be readily

accomplished, the precision of estimating trends is not necessarily increased due to the necessity

of estimating multiple sources of variation. Furthermore, lack of precise estimates of method

error makes it difficult to estimate the population variation of V. parahaemolyticus densities

(i.e., true variation in the absence of method error). Consequently, to maintain consistency only

data from DePaola et al. (38) was used in the harvest module of this risk assessment.

The distributions of total V. parahaemolyticus densities in water and oyster samples were

positively skewed. This is consistent with the almost universal observation that microbial

populations in foods are lognormally distributed. Therefore the logarithm of the density being

more normally distributed was regressed against temperature and salinity. V. parahaemolyticus
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was not detected in a relatively large proportion of samples (e.g. 19 of 61 oyster samples

(31%)). Some of these samples are likely to have been false-negatives due to limitations of the

method. In order to avoid upward bias of predicted levels at low temperatures the estimate of

the regression line of log10 total V. parahaemolyticus/g oyster meat was obtained by the

censored or Tobit regression method. The Tobit regression is a maximum likelihood procedure

with likelihood reflecting both the probability of obtaining a nondetectable outcome at a given

temperature as well as the probability distribution of observable densities given that a sample

has detectable V. parahaemolyticus. The effect of this likelihood structure is to weight the

influence of nondetectable outcomes on estimated trends differently in comparison to samples

with quantifiable densities. The influence of nondetectable outcomes is based on the probability

of the density of a sample falling below a fixed limit of detection rather than the assumption that

a nondetectable measurement corresponds to an observed and quantifiable density at the limit of

detection or one-half the limit of detection as is commonly assumed.

In the reanalysis of the DePaola et al. study (38), the effect of temperature on mean log10 total

V. parahaemolyticus densities was found to be approximately linear over the range of

environmental water temperatures. The presence of a quadratic effect in temperature was not

evident (i.e., not significant). With regard to salinity, a quadratic effect was found to be

significant, suggesting that V. parahaemolyticus increase with increasing salinity up to an

optimal level and then decrease with increasing salinity thereafter. There was no significant

interaction between temperature and salinity evident based on the data. Consequently, the best

fitting model obtained was of the form

=a + b∗ + g ∗ + g ∗ + e

2

1 2

log(Vp / g) TEMP SAL SAL

where TEMP denotes temperature in °C; SAL denotes salinity in parts per thousand (ppt); ", $,

(1, and (2 are regression parameters for temperature and salinity effects on mean log10 densities,

and , is a random normal deviate with zero mean and variance F

2

 corresponding to the

combined effects of population and method error variation.

The resulting parameter estimates were

1.0

0.004

0.18

0.12

2.6

2

2

1

=

= −

=

=

= −

s

g

g

b

a

The estimated relationships between total V. parahaemolyticus densities in oysters versus water

temperature and salinity are shown in Figures IV-2 and IV-3, respectively.
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0.5

1.5

2.5

3.5

4.5

0 5 10 15 20 25 30 35

Water temperature (o C)

Vp density in oysters (log Vp/g)

limit of

detection

Figure IV-2. Observed log10 V. parahaemolyticus (Vp) densities in oysters versus water

temperature at different salinities.

(<10 ppt (Å), 10 to 20 ppt (>) and >20 ppt (è) in comparison to model predicted effect of

temperature on mean log10 density (solid line) and 95% confidence limits (dashed lines) at

salinity of 22 ppt).

Both salinity and temperature effects were significant based on the regression. The variation of

observed values about the predicted mean regression line shown in Figure IV-2 is attributable to

the effects of salinity as well as the variation about the mean due to population variation and

method error. This regression line gives the predicted mean levels versus temperature at a

predicted optimal salinity of 22 parts per thousand (ppt). Similarly the variation of the observed

data about the regression curve (parabola) for salinity effect shown in Figure IV-3 is partially

attributable to differences in water temperature in addition to population and method error

variation about the mean.

Extremes of salinity below 5 ppt are known to be detrimental to survival of V.

parahaemolyticus. However, the influence of salinity within a range of moderate environmental

salinities (i.e., 5-35 ppt) is not as clear. Based on the regression analysis, a quadratic

relationship for V. parahaemolyticus densities versus salinity within the 5-35 ppt range is

consistent with the DePaola et al. data (38). However, this projected effect of salinity is not as

strong as that of temperature. Within a broad range around the optimal salinity of 22 ppt, the

results of the regression suggest that the differences in salinity actually encountered in oyster

harvesting have relatively little effect on the V. parahaemolyticus population (Figure IV-4).
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0.5

1.5

2.5

3.5

4.5

0 5 10 15 20 25 30 35

Salinity (ppt)

Vp density in oysters (log Vp/g)

limit of

detection

Figure IV-3. Observed log10 V. parahaemolyticus (Vp) densities in oysters versus salinity

at different temperatures.

(<15E C (Å), 15 to 25E C (>), and >25E C (è) in comparison to model predicted effect of

salinity on mean log10 density (solid line) and 95% confidence limits (dashed lines) at

temperature of 19E C).

Clearly, in order to predict the distribution of V. parahaemolyticus densities at harvest based on

the regression and the projected influence of water temperature and salinity in the environment,

representative distributions of water temperature and salinity need to be estimated. Based on

near-shore buoy data available from the National Buoy Data Center, regional and seasonal

distributions of water temperature were available. However, representative data concerning the

variation of salinity in shellfish growing areas were not identified. Consequently the effect of

salinity was not incorporated into the present simulation.

Two considerations suggest that neglecting the effect of salinity does not adversely affect the

predictive value of a model based on temperature alone. First, as shown in Figure IV-4,

predicted mean V. parahaemolyticus densities vary by less than 10% from the optimal

(maximum) density as salinity varies from 15 to 30 parts per thousand (ppt). Secondly,

measurements of oyster liquor salinity at the retail level (44), which are strongly correlated with

salinity of harvest waters (44), suggest that oysters may be harvested from the more saline areas

of the estuaries year round. The mean oyster liquor salinity in the ISSC/FDA survey was found

to be 24 ppt with a standard deviation of 6.5 ppt based on 249 samples. The study was

conducted year round with samples obtained from all regions of the country. These two

considerations suggest that the effect of variation of salinity on predicted distributions of V.
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parahaemolyticus densities would be minor. Variations in salinity between 15 and 30 ppt

would increase the variance of the predicted distribution by only a small amount.

50%

60%

70%

80%

90%

100%

10 15 20 25 30 35

Salinity (ppt)

% of predicted Vp levels at 22 ppt

oyster

water

Figure IV-4. Effect of salinity on predicted mean log10 V. parahaemolyticus (Vp) density in

oysters and water relative to predicted density at optimal salinity (22 ppt).

Neglecting the effect of variations in salinity in the simulation can be accomplished in either of

two ways. Either salinity can be fixed to a mean value (i.e., 22 ppt) in the regression

relationship derived above or the prediction of V. parahaemolyticus densities can be based on a

regression analysis of the DePaola et al. data (38) with water temperature as the only effect in

the model. With water temperature as the only effect the regression equation is:

log(Vp / g) =a+ b∗TEMP + e

where TEMP denotes temperature in ° C, " and $ are regression parameters for temperature

effect on mean log10 densities, and , is a random normal deviate with zero mean and variance

F

2

 corresponding to the combined effects of population and method error variation.

Parameter estimates obtained based on the Tobit estimation method are

1.1

0.12

1.03

2

=

=

= −

s

b

a
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Based on the data, the estimate of the variance about the mean (σ

2

) is an inflated estimate of

population variation due to method error. An estimate of population variation about the mean is

obtained by subtracting out an estimate of the method error. The membrane filtration method

used in the DePaola et al. study (38) was the HGMF procedure developed by Watkins et al.

(140) and latter revised by Entis (41). When all suspect colonies are tested for confirmation, the

precision of the HGMF method has been shown to be somewhat greater than the 3 tube MPN

(most probable number) procedure (41, 140). In the DePaola et al. study (38), enumeration of

V. parahaemolyticus colonies was based on testing of five suspect colonies. Consequently,

enumeration was not as precise as possible and overall method error associated with estimating

V. parahaemolyticus densities may have been more comparable to that of a 3 tube MPN

procedure. An estimate of the method error variance of the 3 tube MPN procedure is 0.35 (39)

and this value was considered a reasonable estimate of the method error for the DePaola et al.

study (38).

The predicted mean log V. parahaemolyticus level versus temperature for the temperature only

regression is shown in Figure IV-5. Clearly, this relationship is comparable to that which would

be obtained by fixing the salinity to a near optimal value (22 ppt) in the prediction equation

based on both water temperature and salinity. The temperature only regression was used to

model the relationship between temperature and density of total V. parahaemolyticus at time of

harvest.

0.5

1.5

2.5

3.5

4.5

0 5 10 15 20 25 30 35

Water temperature (o C)

Vp density in oysters (log Vp/g)

limit of

detection

Figure IV-5. Observed log10 V. parahaemolyticus (Vp) densities in oysters versus water

temperature at different salinities.

(<10 ppt (Å), 10 to 20 ppt (>) and >20 ppt (è) in comparison to predicted log10 densities

(solid line) and 95% confidence limits (dashed lines) based on temperature only regression

model).
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Water Temperature Distributions

Regional and seasonal distributions of water temperatures were developed based on

accumulated records from coastal water buoys (National Buoy Data Center (NBDC) data).

Seasons were defined by calendar month: winter (January through March), spring (April

through June), summer (July through September), and fall (October through December). For

each region and season a shallow water buoy was selected as being representative of the water

temperature distribution for oyster harvest areas within that region/season combination. The

available database for most buoys has hourly water temperatures from 1984 up to the present,

with occasional data gaps due to instrumentation malfunction. The correlation between water

temperature and the ambient air temperature that oysters are subject to after they are harvested

was accounted for by selecting buoys for which air temperature records were also available.

Considering that oyster harvesting outside of the Pacific Coast region commences early in the

morning and ends mid or late afternoon, the daily water temperature recorded at noon was

considered to represent an average daily temperature.

The distribution of these "average" temperatures within a given region and season varies from

year-to-year with wider variations occurring during the transitional seasons of spring and fall.

Within a given year, the distribution of the noontime water temperature was found to be

unimodal within a given range. This empirical distribution is adequately approximated as a

normal distribution provided that no weight is given to implausible values outside the historical

range of values that may be expected. Differences in these distributions from one year to the

next are evident in the buoy data. We have characterized this year-to-year variation in the water

temperature distributions by calculating the central tendency and variation in both the mean and

standard deviation of these distributions. The buoys selected and the summary statistics

calculated are shown in Table IV-1.
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Table IV-1. Summary statistics (mean, variance and correlation) of the year-to-year

variation in the mean and standard deviation of noontime water temperature distributions

for different regions and seasons

Seasonal Water Temperature Distributions (°C)

Region

Winter

(Jan - March)

Spring

(April - June)

Summer

(July - September)

Fall

(October - December)

Northeast Atlantic

(Ambrose buoy, NY

harbor)

mean(μ) a = 4.51

mean(σ) = 1.23

variance(μ) = 1.04

variance(σ) = 0.23

corr(μ,σ) = -0.14

mean(μ) = 12.0

mean(σ) = 4.2

variance(μ) = 0.74

variance(σ) = 0.34

corr(μ,σ) = 0.57

mean(μ) = 20.7

mean(σ) = 1.34

variance(μ) = 0.86

variance(σ) = 0.22

corr(μ,σ) = -0.25

mean(μ) = 12.0

mean(σ) = 3.37

variance(μ) = 0.73

variance(σ) = 0.36

corr(μ,σ) =-0.08

Mid-Atlantic

(Thomas Point

Lighthouse buoy,

Chesapeake Bay)

mean(μ) = 3.92

mean(σ) = 1.92

variance(μ) = 1.0

variance(σ) = 0.21

corr(μ,σ) = -0.31

mean(μ) = 16.8

mean(σ) = 5.1

variance(μ) = 0.56

variance(σ) = 0.34

corr(μ,σ) = -0.16

mean(μ) = 25.0

mean(σ) = 1.8

variance(μ) = 0.25

variance(σ) = 0.12

corr(μ,σ) =0.47

mean(μ) = 11.6

mean(σ) = 5.1

variance(μ) = 1.0

variance(σ) = 0.85

corr(μ,σ) =-0.28

Gulf Coast

(Dauphin Island,

AL buoy)

mean(μ) = 14.2

mean(σ) = 2.7

variance(μ) = 1.54

variance(σ) = 0.27

corr(μ,σ) = -0.08

mean(μ) = 24.5

mean(σ) = 3.5

variance(μ) = 0.98

variance(σ) = 0.27

corr(μ,σ) =-0.55

mean(μ) = 28.9

mean(σ) = 1.5

variance(μ) = 0.11

variance(σ) = 0.11

corr(μ,σ) = -0.41

mean(μ) = 17.9

mean(σ) = 4.5

variance(μ) = 3.2

variance(σ) = 0.55

corr(μ,σ) =-0.53

Pacific Northwest

(Washington State

Shellfish

Specialists)

mean(μ) = 8.1

mean(σ) = 1.62

variance(μ) = 0.76

variance(σ) = 0.13

corr(μ,σ) = 0.01

mean(μ) = 13.7

mean(σ) = 2.4

variance(μ) = 1.0

variance(σ) = 0.24

corr(μ,σ) =0.7

mean(μ) = 17.4

mean(σ) = 2.4

variance(μ) = 0.60

variance(σ) = 0.16

corr(μ,σ) =-0.13

mean(μ) = 10.7

mean(σ) = 2.8

variance(μ) = 0.16

variance(σ) = 0.13

corr(μ,σ) = 0.36

Source of data: National Buoy Data Center (NBDC)

http://www.seaboard.ndbc.noaa.gov/Maps/Wrldmap.shtml

and Washington State shellfish specialist N. Therien, personal communication (131)

NBDC measures surface water temperature (sensors are generally 1.0 to 1.5 meter deep)

a

μ and σ denote mean and standard deviation of within region/season temperature distribution, respectively;

mean(), variance(), and corr() denote the mean, variance and correlation between the parameters μ and σ

across different years
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In Table IV-1, μ and σ denote the population mean and standard deviation of the distribution of

water temperatures within any particular year for different region and season combinations.

The extent of year to year variation of these distributions is summarized by the mean and the

variance of the parameters μ and σ. The mean and variance of these parameters are denoted in

the table as mean(μ), variance(μ), mean(σ) and variance(σ), respectively. The correlation

between μ and σ is denoted by corr(μ, σ). A positive correlation between parameters μ and σ

summarizes the observation that when the mean water temperature is higher than normal the

variation in temperatures from one day to the next is generally greater than that observed when

the mean temperature is lower than normal. Similarly, a negative correlation summarizes the

observation that temperatures are less variable when the mean water temperature is higher than

normal.

For example, the NBDC buoy located at Dauphin Island, Alabama was chosen as representative

of water temperatures for the Gulf Coast. Among other meteorological parameters, this buoy

has recorded water and air temperatures from 1987 to the present time. In reference to Table

IV-1, for the spring season (defined as April through June), the distribution of noontime water

temperature was found to vary from year to year with a typical (or average) mean of 24.5o C

[mean(μ)]. The variance of the mean from one year to the next was 0.98o C [variance(μ)] which

corresponds to a standard deviation of 0.99o C. Similarly, for the standard deviation of the

within year temperature distributions, the central tendency across different years was an average

of 3o C [mean(σ)] with a variance of 0.27o C [variance(σ)]. The correlation between μ and σ

[corr(μ, σ)] was -0.55 indicating that the day-to-day temperatures were less variable when the

overall mean temperature was higher than that of a typical year.

For the Pacific Coast there were no near-shore NBDC buoys recording water temperatures that

could be considered representative of oyster growing areas. Consequently, for this region,

seasonal and year-to-year variations in water temperature distributions were developed based on

compiled data from WA State shellfish specialists (Washington State Department of Health)

from 1988 through 1999. These water temperature data were recorded in association with

collection of samples for monitoring of vibrios and fecal coliforms and are therefore directly

representative of temperatures for oyster growing areas. Averages of water temperature were

substituted when multiple measurements were recorded for any given day. Year-to-year

variations in the water temperature distributions for the Pacific Coast were developed in the

same manner as that for the other regions.

Additional sources of information concerning water temperatures (and salinity) in oyster

growing areas include the EPA STORET (Storage and Retrieval of U.S. Waterways Parametric

Data) database (http://www.epa.gov/OWOW/STORET/) and the National Estuarine Reserve

Sites (NERR) program (http://inlet.geol.sc.edu/cdmoweb/home.html). In comparison to the

NBDC sites, STORET and NERR are more specific to estuaries as opposed to open coastal

waterways. Some NBDC sites such as Thomas Point Lighthouse (Chesapeake) are located

within estuaries but similar sites could not be identified for the Gulf Coast and Northeast

Atlantic within the NBDC database. Comparison of NERR data for Weeks Bay, AL versus that

of the Dauphin Island NBDC buoy suggests that shallow water estuaries may be slightly

warmer than open coastal waters but that the difference is not substantial (i.e., ~1o C difference
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on average). An additional consideration is the availability of enough long-term historical data

to determine extent of year-to-year variation. As already indicated, data is available from most

NDBC buoys from 1988 to the present. The NERR program only started data collection in

1995. Although STORET has considerable long term historical data associated with monitoring

of water quality dating back to 1964, access to STORET records is not readily available at

present and the data could not be accessed during the time frame of the risk assessment. Also,

STORET records do not necessarily correspond to fixed locations, as is the case for NBDC and

NERR.

Additional data on water temperature (and salinity) measurements specific to oyster harvesting

areas were made available to the risk assessment team by State agencies in Texas, Alabama,

New York, and Connecticut. Water temperatures provided were not substantially different from

the NBDC data selected for each region.

Prediction of the distribution of pathogenic V. parahaemolyticus densities

Table IV-2 shows estimates of the percentage of total V. parahaemolyticus isolates that have

been found to be pathogenic in several studies. The estimate based on studies by Kaysner and

colleagues applies to the Pacific Northwest (76) with the other estimates in Table IV-2 being

appropriate for all other areas of the country. The estimates suggest that the average percentage

of V. parahaemolyticus that are pathogenic relative to total V. parahaemolyticus, on the West

Coast is ~3% and that the average percentage pathogenic in the Gulf Coast and other areas of

the country is 0.2 to 0.3%.
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Table IV-2. Estimates of pathogenic V. parahaemolyticus (Vp) as a percentage of total V.

parahaemolyticus

Oyster samples (12 oyster composites)

containing detectable pathogenic V.

parahaemolyticus (TDH+ or KP+)a

V. parahaemolyticus isolates that are

TDH+ or KP+

Number

oyster

samples with

pathogenic

Vp

Number

tested

Percent oyster

samples with

pathogenic Vp

Number

isolates

pathogenic

Number

isolates

tested

Percent

isolates

pathogenic

Source

8 TDH+ 193 4.1 9 TDH+ 3233 0.3% ISSC/FDA retail study

(unpublished) (44)

NDb153

oyster,

water,

&

sedimen

t

samples

tested

for KP+

ND 4 KP+ 2218 0.18% Galveston Bay , TX (133)

4 TDH 25 16 10 TDH 308 3.2% Grays Harbor, WA (73)

Puget Sound, WA (76)

3 TDH 96 3.1 10, 140,

and 10

cfu/g in

three

samples

ND 0.3% FDA study of Texas

outbreak Galveston Bay,

TX (37)

a KP+ - Kanagawa-positive; TDH+ - thermostable direct hemolysin-positive, a toxin produced

by Vibrio parahaemolyticus that lyses red blood cells in Wagatsuma agar. These terms are

interchangeable in defining pathogenicity of V. parahaemolyticus

b ND = not determined

There is considerable uncertainty with regard to the average percentage of total V.

parahaemolyticus isolates that are pathogenic due to the relatively small sample sizes for

estimating such a small percentage. Furthermore, this percentage is likely to vary somewhat

from one year to the next. Even if an average percentage were known with certainty, this

information together with the estimated distributions of total V. parahaemolyticus densities is

not sufficient to identify the distribution of pathogenic V. parahaemolyticus densities. It is

likely that the density of pathogenic strains is spatially and temporally clustered in the

environment to some degree. The average number of isolates that are pathogenic does not

identify the extent of this clustering.
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To account for the probable spatial and temporal clustering of pathogenic strains relative to total

V. parahaemolyticus densities, we have assumed a beta-binomial distribution for the number of

pathogenic V. parahaemolyticus at the time of harvest. Under a beta-binomial distribution the

percentage of total V. parahaemolyticus which are pathogenic varies from one sample of oysters

(e.g. 12 oyster composite) to the next. Given the occurrence of outbreaks this appears to be a

reasonable assumption but cannot be validated directly since extensive quantitative surveys of

pathogenic V. parahaemolyticus densities are not available. Specifically, based on the number

of total V. parahaemolyticus (Vptotal), within a given composite, the number of pathogenic

(Vppath) present is assumed to be distributed as a binomial random variable with Vptotal trials

(size parameter) and a probability of success (p) distributed as a beta random variable. The

distribution of the probability parameter p is called a mixing distribution and the variation of

this parameter across composites of oysters induces a clustering of pathogenic strains relative to

total V. parahaemolyticus.

Formally this beta-binomial model is expressed as:

Vp | (Vp n) ~ B(n, p) Beta(a, b)

p

path total = ∧

The notation here indicates that the distribution of the number of pathogenic V.

parahaemolyticus present is conditional on the number of total V. parahaemolyticus present (n).

The mean and variance of this conditional distribution are:

( )











+ −

+

=





























−

+ +

+

+

= =

+

= =

1 *( 1)

( )

*

*

( 1)

1

1

1

( )

*

[ | ] *

[ | ] *

2

2

n n

Var Vp Vp n n n

E Vp Vp n n

path total

path total

f

a b

a b

a b a b

a b

a b

a

where E[C] and Var[C] denote the mean and variance, respectively. The parameter f is called

the overdispersion parameter. The parameters a and b of mixing distribution in the betabinomial can expressed in terms of the average percentage of isolates which are pathogenic (P),

which is the mean of the mixing distribution, and the dispersion parameter f:

f

f

b

f

f

a

(1 ) * (1 )

* (1 )

− −

=

−

=

P

P
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From Table IV-2, best estimates of the parameter P are 0.03 for the West Coast and 0.002 for

other regions of the country. The information is more limited with respect to the value of the

shape parameter f. This parameter pertains to the variation of frequency of pathogenic V.

parahaemolyticus across different oyster samples or composites. Based on the data on

frequency of pathogenic isolates, Bayes estimates of the parameters a and b are:

ˆ 1

ˆ 1

= − +

= +

n r

r

b

a

where r is the number of pathogenic isolates and n is the total number of isolates. These

estimates differ for the West Coast versus other regions of the country in the same manner as

does average percentage pathogenic (P). An estimate of the dispersion parameter is:

2

1

1

ˆ

ˆ

1 ˆ

+

=

+ +

=

a b n

f

Based on the data shown in Table IV-2, estimates of the dispersion parameter are 0.0032 for the

West Coast and 0.00045 for the other regions of the country.

To the extent that the average percentage of isolates that are pathogenic is uncertain, and may

vary from year to year, P was evaluated as an uncertainty parameter in the Monte Carlo

simulations. The uncertainty was modeled as a triangle distribution with a different mean and

range for the Pacific Northwest than for other regions of the country. For the Pacific Northwest

the average percentage pathogenic was estimated to be 3% and the minimum and maximum of

the distribution was taken to be 2% and 4%, respectively. For all other regions of the country

the average percentage pathogenic was estimated to be 0.2% and the corresponding minimum

and maximum of the distribution was 0.1% and 0.3%, respectively. Uncertainty with regard to

the shape parameter f was not evaluated.

Overall, the Monte Carlo simulation of the distribution of pathogenic V. parahaemolyticus

present in oysters was preformed as follows. For each region and season the mean and standard

deviation of water temperature distributions were sampled based on the bivariate normal

distributions given in table IV-1. Each random sample from these distributions represents a

distribution of water temperature (i.e., for different years). Given a water temperature

distribution, the distribution of total V. parahaemolyticus densities in composites of 12 oysters

at harvest was simulated by (a) sampling from the distribution of water temperature; (b) using

the regression relationship to calculate a mean density corresponding to each sampled water

temperature; and (c) perturbing the calculated means by a random normal deviate corresponding

to the estimate of population variation of the densities. The distribution of pathogenic V.

parahaemolyticus densities was derived from that of total V. parahaemolyticus assuming a betabinomial model for the extent of clustering of pathogenic relative to total counts. Multiple

simulations were run with different values of average percentage of isolates pathogenic in order

to evaluate the uncertainty with regard to this parameter.
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Post Harvest Module

The Post Harvest Module describes the effects of typical industry practices, including

transportation, handling and processing, distribution, storage, and retail, from harvest to

consumption, on V. parahaemolyticus densities in oysters harvested from various locations and

seasons. Factors considered as possible influences on the levels of pathogenic V.

parahaemolyticus at consumption include: ambient air temperatures at time of harvest; time

from harvest until the oysters are placed under refrigeration; time it takes the oysters to cool

once under refrigeration, and length of refrigeration time until consumption. This module also

describes possible intervention strategies, such as mild heat treatment, freezing, hydrostatic

pressure, depuration, and relaying, which could reduce V. parahaemolyticus densities.

Although the ecology of V. parahaemolyticus has been studied extensively (69, 70), little is

known about the growth and survival of V. parahaemolyticus in shellstock oysters (30) or the

effectiveness of mitigations aimed at reducing V. parahaemolyticus levels. The effects of post

harvest storage on V. vulnificus growth in oyster shellstock (26, 27), and the effectiveness of

various mitigation strategies for reducing V. vulnificus have been studied more extensively (29,

42, 99, 113, 122). Similar approaches are currently under investigation for V. parahaemolyticus

and some preliminary data are included in this section.

The National Shellfish Sanitation Program (NSSP) time/temperature matrix for control of V.

vulnificus requires oyster harvesters from any state, which previously had two or more

confirmed cases of V. vulnificus to refrigerate oysters within 10 hours (h) after harvest during

summer months, depending on water temperature. This provides approximately 10-fold

reduction in V. parahaemolyticus growth relative to 20 h required in other months and on other

coasts.

Mitigation Strategies

Proposed mitigation processes such as mild heat and freezing, which have been shown to be

effective in reducing V. vulnificus levels, would probably have a similar effect on V.

parahaemolyticus but only limited data is currently available (29). Other possible strategies

include irradiation, high pressure, depuration and relaying. However, we have no relevant data

on their effectiveness on V. parahaemolyticus in shellstock oysters, and had to rely on data from

studies on V. vulnificus.

Reducing time to refrigeration

• It has been shown from the literature that a reduction in the extent of growth of 0 to 10,000-

fold in V. parahaemolyticus densities could be achieved depending on the initial V.

parahaemolyticus levels, ambient air temperature and time to refrigeration (30, 51, 66, 67).
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Mild heat treatment

A 6-log reduction of natural V. vulnificus population was achieved by heating shucked oysters

for 5 min at 50° C (29). Similar heat sensitivity was observed between V. parahaemolyticus and

V. vulnificus (51). Assuming that V. parahaemolyticus responds similarly to heat as. V.

vulnificus, a 4.5 to 6-log (1,000,000-fold) reduction of V. parahaemolyticus densities could be

expected by treating oysters for 5 min at 50° C.

Freezing treatment

A 1973 study reported a two-stage mortality for V. parahaemolyticus with an initial stage of

cold shock followed by a second stage related to the frozen storage conditions (66). Estimates

of effect of cold shock and frozen storage conditions were obtained by regression analysis of the

observed data. Based on the analysis, freezing combined with frozen storage for 30 days at –

30° C and –15° C is projected to result in a 1.2 and 1.6 log10 reduction of V. parahaemolyticus

numbers in oysters, respectively. A similar decline (2 to 3 logs) of V. parahaemolyticus

(natural population and dosed with pathogenic O3:K6 serotype) was observed in oysters frozen

35 days at –20° C (25). Freezing combined with frozen storage for 30 days would be expected

to produce approximately a 2 log reduction of pathogenic V. parahaemolyticus. Both

pathogenic strains (TDH+) and non pathogenic (TDH-) V. parahaemolyticus respond similarly

to freezing (25).

Depuration

In the United States, depuration is conducted exclusively with UV light disinfection (113).

There is a broad spectrum of conditions under which shellfish are depurated. Optimal times,

temperatures and salinities for effective depuration vary among shellfish species. Published

literature has shown that depuration appears to have no significant effect on decreasing the level

of Vibrio spp. in naturally infected oysters or clams, and these microbes may even multiply in

depurating shellfish, tank water, and plumbing systems (42, 53). A 1 log reduction of V.

parahaemolyticus was observed in the hardshell clam, Mercinaria mercinaria, after 72 h of

depuration at room temperature (53), and >2 log reduction at 15o C (52). Son and Fleet (122)

observed a 5 log reduction in lab-infected oysters (from 9x107 to 8x102 in 72 h). Eyles and

Davey (42) showed no difference (p<0.1) before and after depuration in naturally infected

oysters.

Relaying

Relaying is the process by which shellfish are cleansed by transferring them to clean shellfish

growing areas. There is little data available on this approach, which is also problematic as V.

parahaemolyticus is ubiquitous in estuarine environments. Son and Fleet (122) demonstrated a

decrease from 18 V. parahaemolyticus/g to < 5 V. parahaemolyticus/g after 6 days.
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Modeling of the Post Harvest Module

The purpose of modeling the Post Harvest Module is to simulate the effects of typical industry

practices on the levels of V. parahaemolyticus in oysters from harvest to consumption for

various locations and seasons. The module also simulates the effect of intervention strategies.

The input to the module is the regional and seasonal distributions of total and pathogenic V.

parahaemolyticus at harvest. The output of the module is a series of predicted distributions of

the total and pathogenic densities at time of consumption. Figure IV-6 represents a

diagrammatic representation of the parameters modeled in this section. The baseline prediction

is the distribution of density of V. parahaemolyticus (in 12 oyster composites), assuming current

industry practices and no intervention.

Figure IV-6. Schematic depiction of the Post Harvest Module of the V. parahaemolyticus

(Vp) risk assessment model.

The principle assumption used to develop the relationships between densities at harvest and

densities at time of consumption is that the growth and survival of pathogenic V.

parahaemolyticus is the same as total V. parahaemolyticus. Although no definitive studies of

the growth characteristics of pathogenic V. parahaemolyticus are available, preliminary data

suggest that there is little difference between growth characteristics of pathogenic versus

nonpathogenic strains (37). Furthermore, observation of the growth of total V.

parahaemolyticus in oysters is limited to only one temperature (26o C). To bridge this data gap

we have used a model of V. parahaemolyticus growth in broth developed by Miles et al. (95).

The predictions of this model have been adjusted to predict the growth rate in oysters, which is

less than that of broth model systems possibly due to the influence of competing microflora.

Vp/g at harvest Time to refrigeration

Air temperature

Vp/g at 1st refrigeration

Cooldown time

Vp/g at cooldown

Storage time

Vp/g at consumption
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Growth of V. parahaemolyticus from harvest to first refrigeration

The extent of growth that occurs during the period of time from harvest until the time that

oysters are first placed under refrigeration is determined by three factors: (a) the growth rate of

V. parahaemolyticus as a function of temperature; (b) the temperature of oyster meat following

harvest and (c) the length of time held unrefrigerated.

Growth Rate Model

Miles et al. (95) modeled the growth rate of V. parahaemolyticus based on studies of four

strains at different temperatures and water activity, which is a measure of the availability of free

water in the broth model system. Worst case estimates of growth were obtained based on the

fastest growing of the four strains studied. For each combination of temperature and water

activity, the extent of bacterial growth observed was modeled using the Gompertz function.

This is a sigmoid growth curve with a growth rate (slope) monotonically increasing up to a

maximum and then falling to zero as the bacterial population reaches a steady-state. The

maximal rate of growth (μm) is the most relevant summary of the fit because the growth rate

approaches the maximal growth rate rapidly and does not decline significantly until steady-state

is reached.

A secondary model was used to estimate the effect of environmental parameters on the maximal

growth rate. This model was assumed to be of the square root type:

[{ } ]

ln(10)

*( )* 1 exp( *( )) * ( )*[1 exp( *( ))] min max w w,mix w w,max

m

b T −T − c T −T a − a − d a − a

m =

where

μm = maximal growth rate (log10 per minute)

aw = water activity

T = temperature (in degree Kelvin)

Based on the data from the fastest growing strain the estimates of the parameters were:

b = 0.0356

c = 0.34

Tmin = 278.5

Tmax = 319.6

aw,min = 0.921

aw,max = 0.998

d = 263.64

The parameters Tmin, Tmax, aw,min, and aw,max denote the range of temperatures and water

activity over which growth can occur. The authors validated their model by comparison of

model predictions with observed rates in eight other studies of growth in broth model

systems obtained from the literature.
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A plot of the resulting model prediction for μm as a function of either temperature or water

activity is a unimodal function with a maximum value and zero growth rate outside of the

predicted range of temperatures and water activity favorable for growth. To use this

equation as a prediction of growth rate in oysters we assumed that water activity of oysters

does not vary substantially and have fixed this parameter to the optimal value of 0.985

predicted for the broth model system. At this water activity, the predicted growth rate in

broth at 26o C is 0.84 log10 per hour which is approximately a 7-fold increase in density per

hour. This is four times greater than the rate of growth observed for V. parahaemolyticus in

oysters held at 26o C (51).

Based on this observation, our best prediction of the growth rate in oysters at temperatures

other than 26o C was obtained by dividing the predicted rate for broth model by a factor of

four. This assumes that the growth rate in oysters is a constant fraction of the growth rate in

broth at all temperatures. We have evaluated the influence of this assumption in the risk

assessment by considering this factor as an uncertainty parameter varying according to a

triangle distribution in the range of 2 to 8 with a mean of 4. This evaluates the sensitivity of

our conclusions to the magnitude of the relative growth rate in oysters versus broth model

but does not fully address the uncertainty in so far as it is conceivable that the relative

growth rate could be temperature dependant.

The use of the Gompertz function by Miles et al. (95) to model bacterial growth in broth is

appropriate. After transfer of an inoculum to different medium or environmental conditions

there is a demonstrable lag phase during which the bacterial population adapts to different

environmental conditions and growth is suboptimal (24). However, the Gompertz is not an

appropriate model for growth of V. parahaemolyticus in oysters after harvesting, as changes

in environment are typically gradual and do not arrest the growth rate and induce a lag

phase. Consequently, for oysters, the extent of growth occurring over time at a given

average temperature and predicted maximal growth rate is assumed to follow a simple threephase loglinear model with no lag phase (19). This model is of the form:

log ( ( )) min{log ( (0)) * , } 10 N t = 10 N + mmt A

where N(t) refers to the bacterial density at a given time (t) post harvest, A is the logarithm

of the maximum attainable density of V. parahaemolyticus in oysters, and the parameter μm

is a function of ambient temperature as described above. At 26o C, the density of V.

parahaemolyticus in oysters was observed to approach a plateau of approximately 6.0 log10

per gram after 24 hours (51). We have assumed this value for the maximal density (A) at all

temperatures. Figure IV-7 shows predictions of the log10 increase in V. parahaemolyticus

density from an initial level of 1,000/g as a function of time for three ambient temperatures

(20, 26 and 32o C).

Ideally, the average temperature used to determine the parameter μm in the above equation is

the temperature of oyster meat of shellstock. Clearly the temperature of oyster meat

depends on the temperature of both the air and water at the time of harvest. Temperature of
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the oyster meat after harvest is expected to gradually equilibrate with the temperature of the

air and may be modified somewhat by evaporative cooling and the extent to which oysters

are properly shaded from direct sunlight aboard ship. In the absence of information to the

contrary, we have assumed that the temperature of oyster meat equilibrates rapidly with that

of the ambient air and have therefore used air temperature as a surrogate for oyster meat

temperature. Ambient air temperature data recorded at noon from the near-shore NBDC

buoys representative of various coastal regions were used for this purpose.

3

4

5

6

7

0 5 10 15 20

time post-harvest (hours)

Vp density in oysters (log Vp/g)

26o C

32o C

20o C

Air temperature

Figure IV-7. Predicted loglinear growth of V. parahaemolyticus (Vp) from initial density of

1,000 (3 log10) Vp/g as a function of ambient air temperature.

Distribution of ambient air temperature

Examination of water and air temperatures obtained from the NOAA/NBDC database showed a

strong correlation between water and air temperature. This correlation has been incorporated

into the risk simulation by modeling the distribution of the difference in water versus air

temperatures based on the normal distribution within any given region and season. These

distributions are then used to predict the air temperature that oysters would be subjected to

depending on the water temperature at the time of harvest.
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In the process of simulating the distribution of total and pathogenic V. parahaemolyticus at

harvest by the Monte Carlo method, the water temperature associated with any given outcome is

retained. A corresponding air temperature is predicted by sampling from the appropriate

distribution for the difference in air versus water temperature. This difference is then added to

the water temperature to derive a corresponding air temperature. The distributions of difference

in air versus water temperature were obtained by pooling the data available for each near-shore

buoy across all available years. The mean and variance of these distributions are shown in

Table IV-3.

Table IV-3. Means and standard deviations of the distribution of the difference between

recorded air and water temperatures at midday (° C)

Mean (standard deviation) Distribution Differences

Region between Air and Water Temperature

Winter

(Jan-March)

Spring

(April-June)

Summer

(July-Sept)

Fall

(Oct-Dec)

Northeast Atlantic

(Ambrose buoy, NY harbor)

-2.6 (5.0) 2.2 (3.2) 0.52 (2.7) -3.2 (4.2)

Mid-Atlantic

(Thomas Point Lighthouse

buoy, Chesapeake Bay, MD)

-0.25 (4.0) 0.54 (2.9) -1.4 (2.1) -2.1 (3.1)

Gulf Coast (Dauphin Island,

AL buoy)

-1.07 (3.3) -1.24 (1.63) -1.66 (1.33) -1.62 (3.3)

Pacific Northwest

(based on 3 years of data from

NOAA buoy on north end of

Puget Sound, WA)

-1.6 (1.8) 1.3 (1.3) 1.3 (1.5) -0.8 (2.0)

Source of data: http://www.seaboard.nbdc.noaa.gov/Maps/Wrldmap.shtml

Distribution of time oysters are left unrefrigerated

The distribution of the length of time that oysters are held unrefrigerated was developed by

using the distribution of duration of daily oyster harvesting operations (i.e., length of working

day). The distribution of length of time oysters are left unrefrigerated is derived by assuming

that oysters are harvested uniformly from the start of the harvest up to one hour prior to

conclusion of the harvesting operation when oysters are landed and placed in cold storage.
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Table IV-4 shows the minimum, maximum and mean duration of oyster harvesting that we have

projected for the different regions and seasons. In the risk simulation, we have used Beta-PERT

distributions based on these parameters to simulate the variation in the duration of harvesting.

A Beta-PERT distribution is a translated and scaled Beta distribution with specified moments.

It is commonly used for the purpose of simulating parameter variation within a defined range in

Monte Carlo simulations. Figure IV-8 shows the probability density of the Beta-PERT

distribution with minimum of 2, maximum of 11 and mean of 8 hours.

Table IV-4. Minimum, maximum and mean duration of oyster harvest (length of

harvesting operation) for different regions and seasons

Duration of Harvest (hours)

Location Winter

(Jan-March)

Spring

(April-June)

Summer

(July-Sept)

Fall

(Oct-Dec)

Northeast Atlantic

(assumed same as pre-NSSP Control

plan in Gulf- TX (64))

max = 11

min = 2

mean = 8

max = 11

min = 2

mean = 8

max = 11

min = 2

mean = 8

max = 11

min = 2

mean = 8

Mid-Atlantic

(assumed same as pre-NSSP Control

plan in Gulf- TX (64))

max = 11

min = 2

mean = 8

max = 11

min = 2

mean = 8

max = 11

min = 2

mean = 8

max = 11

min = 2

mean = 8

Gulf Coast - LA (50% of harvest)

(pre-NSSP Control plan in LA in

winter; ICP otherwise (64))

Gulf Coast - FL, AL, TX (50% of

harvest)

(assumed same as pre-NSSP Control

plan in Gulf- TX in winter, NSSP

Control otherwise (64))

max = 13

min = 7

mean = 12

max = 11

min = 2

mean = 8

max = 11

min = 5

mean = 9

max = 10

min = 3

mean = 7

max = 11

min = 5

mean = 9

max = 10

min = 3

mean = 7

max = 13

min = 7

mean = 12

max = 10

min = 3

mean = 7

Pacific Northwest (139) max = 4

min = 1

mean = 3

max = 4

min = 1

mean = 3

max = 4

min = 1

mean = 3

max = 4

min = 1

mean = 3

Source of data: ISSC & FDA (ed.) 1997 National Shellfish (64)

Washington State Shellfish experts and Washington State Department of Health (139)

The parameters for these distributions were developed and based on a 1997 GCSL survey that

included dealer reported statistics on the length of harvest (28). The study was conducted in

several Gulf Coast states during the fall of two successive years; one season prior to initiation of

the NSSP time to refrigeration requirements (for states whose product has been confirmed as the

source of two or more V. vulnificus illnesses), and then the following year after implementation.

Duration of harvest was longer in Louisiana than in Florida and Texas, during both years. This
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probably reflects more remote oyster harvesting areas in Louisiana. The practices of Florida

and Texas were considered to be representative of other regions, and in the absence of

conflicting information, the longer times were assumed for the other regions throughout the

year.

For the Gulf Coast States, we assumed that current harvesting duration is limited in the spring,

summer and fall due to the NSSP time to refrigeration requirements and that duration of harvest

is generally longer in the winter when cooler water conditions prevail. Louisiana, representing

roughly half of the Gulf Coast harvest was treated separately due to the longer duration of

harvest year round. The distribution of harvest duration for the West Coast was not based upon

the GCSL dealer survey in so far as oysters are generally harvested during intertidal periods and

the length of time held unrefrigerated is substantially less. The Pacific Coast Shellfish Growers

Association (PCSGA) stated that Pacific oysters are placed under refrigeration within four hours

and this time is being assumed as the maximum for the Pacific Coast in the absence of survey

data.

0

0.005

0.01

0.015

0.02

0.025

0 2 4 6 8 10 12

Duration of oyster harvesting (hours)

Frequency of duration of oyster 

harvesting at specific hours

Figure IV-8. Beta-PERT probability density distribution for the duration of harvesting

operations during the winter season (Mid-Atlantic, Northeast Atlantic, Gulf

Coast, excluding Louisiana) (44).
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As indicated, harvesting of oysters was assumed to occur uniformly from start of harvest, up to

one hour prior to end of harvest operation. The distribution of the duration of time oysters were

held unrefrigerated, was simulated by first sampling from the distribution for duration of harvest

operation and then sampling from a uniform distribution with a minimum of one hour and

maximum corresponding to the randomly selected duration of harvest. Oysters are harvested at

different times during the length of harvesting operations. Consequently the mean time that

oysters remain unrefrigerated is much less than the maximum length of duration of harvesting

might suggest.

Overall, the extent of growth occurring prior to time of first refrigeration (i.e., the time at which

oysters are first placed in refrigerated storage) was simulated by: (a) sampling air temperature

corresponding to the water temperature at harvest; (b) sampling duration of harvest; (c)

sampling the length of time unrefrigerated given a particular duration of harvest; and then (d)

calculating the extent of growth expected for the given duration of time unrefrigerated.

Excess growth of V. parahaemolyticus during cooldown time

V. parahaemolyticus will continue to grow in oysters after they are placed under refrigeration

until the temperature of the oyster tissues falls below a certain threshold (e.g. 10° C). The time

it takes for oysters to cool once under refrigeration is assumed to be quite variable depending on

efficiency of the cooler, quantity of oysters to be cooled and their arrangement in the cooler.

Data on cooling rates of commercial oyster shellstock could not be located. Preliminary GCSL

experiments with a single in-shell oyster at 30° C in which a temperature probe was inserted

into its tissue indicated a cooling rate of approximately 0.5° C/min when placed into a 3° C

cooler (37). However, 24 oysters in an uninsulated plastic container required approximately 7

hours to drop from 26° C to 3° C. These data suggest considerable uncertainty for cooling times

after oysters are refrigerated and it was concluded that a rectangular distribution between 1 and

10 hours would be appropriate to describe the current state of knowledge.

As oysters cool down to storage temperatures it is reasonable to expect that the growth rate of V.

parahaemolyticus slows with the declining temperature of the oyster tissue. At the start of the

cooldown period, when oysters are first placed under refrigeration, the growth rate is still equal

to the initial rate as determined by ambient air temperature. At the end of the cooldown period,

when oysters have reached storage temperatures, we assume that there is no further growth and

that densities will decline slowly thereafter. Implicitly, this assumes that there is no appreciable

temperature abuse after oysters have been placed in cold storage. The rate at which oysters

cooldown during cold storage is not known. Therefore, in the absence of conflicting

information, we have assumed that during the period of cooldown, the growth rate of V.

parahaemolyticus drops uniformly down to zero.

A discrete approximation of the extent of growth that may occur during cooldown was

simulated by first sampling from a discrete random uniform distribution between 1 and 10 hours

(duration of cooldown). The extent of growth during each hour of the cooldown period was

then approximated by an average growth rate during that hour times a duration of one hour.

The average growth rates were dependant upon the growth rate of V. parahaemolyticus in

oysters left unrefrigerated (i.e., as determined by the ambient air temperature for a given oyster
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lot) and the duration of cooldown. Total excess growth was the sum of these values over the

cooldown period subject to the restriction that the maximum density of 6.0 log10 per gram could

not be exceeded. These calculations are illustrated in the Table IV-5, where, for example, it

takes k hours for a particular oyster lot to reach cooler temperature.

Table IV-5. Discrete approximation of variation in the growth rate of V. parahaemolyticus

during a cooldown period of k hours

Hour of the

cooldown period

Average growth rate

(log10/hr) during the hour

of cooldown

1 m

k

k

m

( + 1) − 1

2 m

k

k

m

( + 1) − 2

3 m

k

k

m

( + 1) − 3

... ...

k m

k

k k

m

( + 1) −

k+1 0

Total excess growth is the sum of the growth over the k hours:

2

1

*

2

1

* ( 1)

1

* ( 1)

( 1)

*

1 1

+

=











 +

= + −













= + −

+ −

∑ ∑ = =

k

k

k

i

k

k

k

k i

m

m

k

i

m

k

i

m

m

m

m m

Since the cooldown time k is a random variable with a mean of 5.5 hours, the average extent of

growth is 3.25*μm, where μm is the maximal growth rate determined by ambient air temperature

at time of harvest. Thus, for an initial growth rate of 0.19 log10 per hour (i.e., at 26o C), the

average growth occurring during cooldown is approximately 0.6 log10.
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Die-off of V. parahaemolyticus during cold storage

Gooch et al. (51) showed that in oysters, V. parahaemolyticus declined 0.003 log10 per hour

when stored 14-17 days at 3° C. This die-off rate was assumed to be typical of all refrigerated

oysters. Error may be introduced because commercial oysters are typically stored at higher

temperatures (5-10° C). Die-off may have been overestimated because chill-stressed V.

parahaemolyticus may not be recovered by the methods used in the study. One of the

enumeration methods employed a repair step in a medium containing magnesium, which has

been shown to increase recovery of chill-stressed cells. This method did not give higher V.

parahaemolyticus counts after refrigeration than did the other methods that were used to

calculate die-off. Therefore, the effect of chill-stress on die-off rate was assumed to be

negligible.

Data from the ISSC/FDA retail study for the time between harvest and sample collection were

assumed to be a reliable estimate for the length of refrigeration time (time between refrigeration

and consumption) (28). Summary statistics on the storage time for samples obtained during the

study are shown in Table IV-6. A small degree of error may be introduced by assuming that

these data are representative of storage time in so far as samples were generally collected on

Monday or Tuesday and most servings are consumed in restaurants on weekends. Since this

was a year long nationwide survey, the mean of 7.7 days and range of 1-21 days was assumed to

be representative of all seasons and regions. In the simulation, we used a Beta-PERT

distribution based on the overall mean, minimum, maximum and mode in order to obtain a

smooth representation of the variation in the duration of storage time.

Table IV-6. Summary statistics of the distribution of storage times (time under

refrigeration in days) of oysters samples obtained during the ISSC/FDA retail study

Storage Time

Consumed locally

(within the same

region of harvest)

Non local

(transported outside

region of harvest)

Overall

Minimum 1 2 1

Maximum 20 21 21

Mean 6.3 9.9 7.7

Mode 6 5 6

Source of data: (44)

The predicted densities of V. parahaemolyticus at time of consumption were therefore simulated

by randomly sampling from the distribution of storage times and multiplying by a die-off rate of

0.003 log10 per hour. The resulting distribution was then subtracted from the predicted

distribution of V. parahaemolyticus densities in oysters initially reaching cooler (no growth)

temperatures.
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Mitigation Strategies

The effects of three possible post harvest mitigations were evaluated in the Monte Carlo

simulations: (a) reduction of time to refrigeration (rapid cooling); (b) heat treatment and (c)

freezing/cold storage.

The mitigation of reduction in time to refrigeration was modeled by assuming that oysters

would be cooled to no growth temperatures immediately following harvest. Immediate cooling

would involve icing or otherwise refrigerating oyster shellstock aboard ship while oyster

harvesting operations continued. Assuming that this mitigation practice was followed without

exception, post harvest growth of V. parahaemolyticus in oysters would occur only during the

period of cooldown required for the oyster meat to reach no growth temperatures. In the

simulation this is accomplished by assuming that the time unrefrigerated is zero (i.e., a

degenerate distribution or constant). However, some growth is still projected to occur during

cooldown as described above.

The effects of heat treatment and that of freezing/cold storage were evaluated by adjusting the

simulated output of the baseline simulation (no mitigation) downward by factors of 4.5 log10

(the lowest level which caused a substantial reduction in illness after mild heat treatment) and 2

log10, respectively. Thus, random sequences of values for total and pathogenic densities

produced in the course of Monte Carlo simulation were divided by 31,623 and 100,

respectively. The implicit assumption here is that the effect of treatment on log10 V.

parahaemolyticus densities is uniform with no induced change in the variance of log10 densities.

The effects of these mitigations on the probability of illness are shown in the Risk

Characterization Section.

Public Health Module

The Public Health Module estimates the distribution of the probable number of illness which

may be expected to occur within any given region and season based on the predicted

distribution of pathogenic V. parahaemolyticus densities at time of consumption, and the

resulting effects on members of the public eating these oysters. Factors taken into account

include the number of V. parahaemolyticus infections, the level of pathogenic V.

parahaemolyticus at consumption, the probability of V. parahaemolyticus infection at different

dose levels, and the number of diarrheal cases as opposed to more serious outcomes such as

septicemia.

Food surveys and oyster landing statistics provide a basis for estimating extent of exposure in

the population. Dose-response relationships can be developed from epidemiological

investigations of outbreaks and sporadic case series, human feeding trials or animal models of

V. parahaemolyticus and related (surrogate) pathogens. The relevant parameters relating to

extent of exposure and dose-response relationship are summarized under three sections:

epidemiology, consumption, and dose-response (hazard characterization).
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Epidemiology

Gastroenteritis due to V. parahaemolyticus infection is usually a self-limiting illness of

moderate severity and short duration (11, 12, 89). However, severe cases requiring

hospitalization have been reported. A summary of clinical features associated with V.

parahaemolyticus gastroenteritis infection is presented in Table IV-7 (12, 89). Symptoms

include explosive watery diarrhea, nausea, vomiting, abdominal cramps, and less frequently

headache, fever and chills. On rare occasions, septicemia, an illness characterized by fever or

hypotension and the isolation of the microorganism from the blood, can occur. In these cases,

subsequent symptoms can include swollen, painful extremities with hemorrhagic bullae (57,

83). Duration of illness can range from 2 hours to 10 days (12, 13).

Table IV-7. Clinical symptoms associated with gastroenteritis caused by V.

parahaemolyticus

Incidence of symptoms Symptoms Median Range

Diarrhea 98% 80 to 100%

Abdominal cramps 82% 68 to 100%

Nausea 71% 40 to 100%

Vomiting 52% 17 to 79%

Headache 42% 13 to 56%

Fever 27% 21 to 33%

Chills 24% 4 to 56%

Source of data: (12, 89)

In Japan, after a decrease in V. parahaemolyticus infections, the incidence started to rise again

in 1994 (9). There were 292 incidents of V. parahaemolyticus involving 5,241 cases in 1996.

In 1997, the incidence increased to 568, with 6,786 cases, and in 1998, there were 850 incidents,

second only to Salmonella infections, but involving more cases than Salmonella (9). In the

United States, outbreaks involving over 700 cases in the Gulf Coast, the Northeast, and the

Pacific Northwest, in 1997 and 1998, were caused by consumption of raw molluscan shellfish,

predominantly oysters, harboring pathogenic V. parahaemolyticus (7, 17, 18). During the

outbreaks, certain serotypes, linked to the consumption of raw molluscan shellfish, particularly

oysters, were identified as important emerging pathogens.

Outbreaks

An outbreak is defined as the occurrence of 2 or more cases of a similar illness resulting from

the ingestion of a common food. The incubation period ranges from 12-96 hours with a median

of approximately 15-24 hours. The number of raw oysters consumed ranges from 1-109

(median of 12); however, the duration of consumption is not known. The typical prevalence of

symptoms for cases with gastroenteritis parallels those that were identified during the Pacific

Northwest outbreak of 1997. These symptoms include diarrhea (99%), abdominal cramps

(88%), nausea (52%), vomiting (39%), fever (33%), and bloody diarrhea (12%). The first
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confirmed case of foodborne illness-associated V. parahaemolyticus infection in the United

States occurred in Maryland in 1971 with an outbreak caused by contaminated steamed crabs

(32). Between 1973 and 1998, forty outbreaks were reported to the CDC from 15 states and the

Guam Territories (34). These outbreaks were all associated with seafood or crosscontamination with raw or undercooked seafood. In 1997, V. parahaemolyticus infection was

confirmed in 209 persons who consumed raw oysters harvested from California, Oregon and

Washington in the United States and from British Columbia in Canada (22). Prior to this

outbreak, the last large outbreak of V. parahaemolyticus infections in North America occurred

in 1981 with 6 culture-confirmed cases (107). In 1998, the largest outbreak in the United States

occurred in Texas in which a total of 416 V. parahaemolyticus infections were associated with

consuming raw oysters harvested from Galveston Bay (34). The first reported outbreak

associated with raw shellfish harvested in New York occurred in 1998 as well, involving 23

culture-confirmed cases (21). For these recent outbreaks, the dates of onset of illness ranged

from May-December with a peak in July-August. Although V. parahaemolyticus outbreaks are

less frequent in occurrence, sporadic cases are not infrequent, as further described below.

Case Reports

Several case reports have been published that outline clinical presentations and outcomes of

patients with V. parahaemolyticus. One such case report describes a 35-year-old woman who

sought medical attention for abdominal pain after she had consumed raw fish (127). She

presented with gastrointestinal symptoms, redness on lower extremities, fever, polyarthritis and

weakness. V. parahaemolyticus was isolated in the stool culture. She was diagnosed as having

reactive arthritis induced by V. parahaemolyticus infection. Another clinical case report

describes a 31-year-old female with a history of alcohol abuse, hepatitis C virus infection, and

cirrhosis (54). She presented with diarrhea, weakness, leg pain, and urine retention. The patient

had ingested raw oysters and steamed shrimp 72 hours prior to admission. V. parahaemolyticus

was isolated from blood samples. The patient developed cardiac arrest and died six days after

presentation.

A suspected case of a laboratory-associated infection was reported in 1972 (117). One day prior

to the development of diarrheal disease the laboratory worker had been handling V.

parahaemolyticus strains for the first time. The illness was associated with severe upper

abdominal pain, bloody stools, nausea and fever. Weakness and abdominal discomfort

continued for 2 days beyond the onset of illness. No other source of V. parahaemolyticus could

be identified, and it was believed that the infection was caused by a relatively small inoculum

(117).

Case Series

A case series is a study of sporadic cases over a period of time. Sporadic cases of V.

parahaemolyticus infections are commonly reported by many states but are primarily reported

by Gulf Coast states. Most V. parahaemolyticus infections present clinically as gastroenteritis,

which has a low case fatality rate. Life threatening septicemia can occur, especially in patients

with underlying medical conditions. The case series has a range of infection throughout the

year, with a peak in September to October. A case series of Vibrio infections related to raw
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oyster consumption was reported in Florida from 1981-1994 (57). Culture-confirmed case

reports of Vibrio infections, reported to the Florida Department of Health and Rehabilitation

Services, were investigated to determine the epidemiology of raw oyster-associated Vibrio

infections. Clinical and epidemiological information from patients was compiled using

standardized Vibrio illness case report forms. Oyster-associated Vibrio infection was defined as

a history of raw oyster consumption in the week prior to onset of gastroenteritis or septicemia.

Incidence rates were calculated using population data from the Florida Office of Vital Statistics.

Estimates of raw oyster consumption were obtained from the Florida Behavioral Risk Factor

Survey, 1988.

The average annual incidence of raw oyster-associated illness from any Vibrio species among

raw oyster-consuming adults over 17-years-of-age was estimated to be 10.1/1,000,000 (95% CI:

8.3-11.9). The annual incidence of fatal raw oyster-associated infections from any Vibrio

species was estimated to be 1.6/1,000,000 oyster-consuming adults (95% CI: 1.3-1.9). In two

epidemiological studies, V. parahaemolyticus accounted for 77 of 339 reported Vibrio infections

(Table IV-8) (57, 83). Of those 77 persons, 68 reported gastroenteritis and 9 had septicemia.

Twenty-nine persons were hospitalized for gastroenteritis with no deaths reported. Eight

patients were hospitalized for septicemia and four of those patients died. Patients with

septicemia had underlying illness including, but not limited to cancer, liver disease, alcoholism

and diabetes mellitus (57, 83).

Table IV-8. Clinical syndromes of raw oyster-associated Vibrio infections in Florida,

1981-1994

Vibrio Species Total Cases Gastroenteritis Septicemia

V. vulnificus 95 13 82

V. parahaemolyticus 77 68 9

V. cholera Non-O1 74 8 66

V. hollisae 38 35 3

V. mimicus 29 29 0

V. fluvialis 19 19 0

Source of data: (57, 83)

In another study, Hlady and Klontz (58) reported that of patients with infections, 25% had preexisting liver disease or alcoholism. These included 75% of the septicemia patients, and 4% of

the gastroenteritis patients. Of the remaining septicemia patients, 9 reported having a history of

at least one of the following: malignancy, renal disease, peptic ulcer disease, gastrointestinal

surgery, diabetes, antacid medication and pernicious anemia. Among the gastroenteritis

patients, 74% had none of the above preexisting medical conditions or had insufficient

information to classify. Thus, while the prevalence of underlying illness was high in the

septicemia patients the majority of patients with raw-oyster associated Vibrio gastroenteritis had

no underlying conditions. Case series data is available through the Gulf Coast Vibrio

Surveillance system, which is a unique regional surveillance system that began in 1989 (89).

Four states participate in this program (AL, FL, TX, LA). Investigators in state and county

health departments complete standardized Vibrio illness investigation forms on all patients from

whom Vibrio isolates are reported. Vibrio reporting comes from individual physicians,
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hospitals, or laboratories. Illness investigation forms contain clinical data concerning signs and

symptoms, underlying illnesses, use of medications, as well as epidemiological information

concerning seafood consumption in the week prior to illness. Information is then forwarded to

the CDC.

During the first year of Vibrio surveillance in 1989, V. parahaemolyticus accounted for 27 of

the 85 reported Vibrio illness characterized by gastroenteritis or septicemia (89). V.

parahaemolyticus was the most prevalent of the Vibrio species reported. Twelve of the 27

persons with V. parahaemolyticus were known to have eaten raw oysters. One person had

septicemia while the remaining 26 persons had gastroenteritis. Oyster-associated infections

occurred throughout the year with the peak occurrence in October.

Based upon CDC surveillance data on V. parahaemolyticus from 1988-1997 in Alabama,

Florida, Louisiana and Texas, the six most common underlying medical conditions associated

with infection include diabetes, peptic ulcer, heart disease, gastric surgery, liver disease and

immunodeficiency (6). For gastroenteritis, 24% of respondents reported one or more of these

six conditions compared with 71% of respondents who had sepsis. In 263 gastroenteritis cases:

7% had diabetes, 6% had peptic ulcer disease, 6% had heart disease, 4% had undergone gastric

surgery, 3% suffered from alcoholism, 3% suffered from some form of immunodeficiency, 3%

had liver disease, 2% had hematological disease, 2% had some form of malignancy, and 1% had

renal disease. Out of 20 septicemic cases, 63% had liver disease, 18% had some form of

immunodeficiency, 18% had peptic ulcer disease, 17% had diabetes, 14% suffered from

alcoholism, 13% had hematological disease, 12% had undergone gastric surgery, 12% had heart

disease, 12% had renal disease, and 11% had some form of malignancy. Among 88 patients

with sporadic V. parahaemolyticus infection and known food histories, 77 (88%) reported

eating raw oysters in the week before illness (34). Of 11 patients with septicemia and known

food history, 10 (91%) had eaten raw oysters. Data from the CDC Gulf Coast Surveillance

System from 1997 to 1998, were limited only to those cases that are both culture confirmed and

ingestion confirmed and resulted in a subset totaling 107 cases. Of these 107 cases, 5 (5%)

involved septicemia in which all five were hospitalized with one death. This is believed by

CDC to be a fairly accurate estimation of the overall incidence of septicemia among cultureconfirmed V. parahaemolyticus infections (6). The presence or absence of underlying

conditions was reported by 4 of the 5 septicemic patients; 3 (75%) of whom had underlying

conditions. Among the 102 cases with gastroenteritis alone, 27 of 90 (30%) respondents

reported being hospitalized for the illness. Patient outcome was reported for 83 patients; one of

whom died. The presence or absence of underlying conditions was reported by 79 persons;

29% of these reported underlying conditions. The underlying conditions included liver disease,

alcoholism, diabetes, malignancy, renal disease, immunodeficiency, hematological disease,

gastric surgery and heart disease.

Based upon active FoodNet data surveillance, CDC estimates that the total number of foodborne

V. parahaemolyticus cases in the United States for 1996, 1997, and 1998 were 2,683; 9,807, and

5568, (rounded to 2,700; 9,800; and 5,600, respectively) (129). These estimates were derived

from the numbers of Vibrio cases reported to FoodNet. For the calculations, the reports of

Vibrio cases with unknown species were included for the estimate of the total number of Vibrio

cases but was not included in the estimate of the percentage of all Vibrio spp. that were V.
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parahaemolyticus. This assumes that the isolates of unknown species are distributed the same

as the isolates of known species. The percentage of V. parahaemolyticus cases attributed to

being foodborne was estimated at 65%. The 1997 estimates are higher as a result of the

increased reporting of cases during the Pacific Northwest outbreak. The variation in estimated

cases from year to year is expected since the numbers obtained from FoodNet are very small.

During the 1972 shrimp-associated V. parahaemolyticus outbreak, a survey revealed that of 72

persons with diarrhea only one sought medical attention (13). Due to underdiagnosing and

underreporting of cases of V. parahaemolyticus, the CDC estimates that the total number of

cases is equal to 20 times the reported cases (94). In a CDC random survey of Gulf Coast

clinical laboratories, only 20% of the laboratories routinely used selective agar for isolating

Vibrio species (34).

Geographic distribution

As mentioned earlier, V. parahaemolyticus was first identified as a foodborne pathogen in Japan

in the 1950s (48). By the late 1960s and early 1970s, V. parahaemolyticus was recognized as a

cause of diarrheal disease worldwide. Prior to 1994, the incidence of V. parahaemolyticus

infections in Japan had been declining, however, from 1994 to 1995 there were a total of 1,280

reports of infection due to V. parahaemolyticus (9). During this time period, the incidents of V.

parahaemolyticus food poisoning outnumbered those of Salmonella food poisoning. For both

years, the majority of the cases occurred in the summer, with the largest number appearing in

August. Food poisoning due to V. parahaemolyticus in Japan is usually restricted to relatively

small-scale outbreaks involving fewer than 10 cases. From 1996-1998, there were 496

outbreaks, 1,710 incidents and 24, 373 cases of V. parahaemolyticus reported. The number of

cases of V. parahaemolyticus food poisoning cases doubled in 1998 as compared to 1997 and

again exceeded the number of Salmonella cases (9). Similar to the 1994-1995 period, outbreaks

were more prevalent in the summer with a peak in August with few outbreaks during winter

months. Boiled crabs caused one large-scale outbreak, involving 691 cases. The majority of

outbreaks were small in scale but occurred frequently. The increased incidence during 1997-

1998 has been attributed to an increased incidence of serovar O3:K6.

A hospital-based active surveillance study of V. parahaemolyticus infections in Calcutta, India,

was conducted from 1994-1996, and identified 146 patients (109). The incidence suddenly

increased in February of 1996 and remained elevated until August of that year when

surveillance ended. The increased incidence of V. parahaemolyticus infections was associated

with an increased prevalence of O3:K6 strains. This serovar had not been isolated in Calcutta

prior to February of 1996. The incidence of diarrhea due to V. parahaemolyticus strain O3:K6

accounted for 63% of the strains isolated from patients in Calcutta between September 1996 and

April 1997. The virulence of the O3:K6 strains isolated from travelers arriving in Japan from

Southeast Asian countries was indistinguishable from O3:K6 strains found in Calcutta, India

(92).

Implicated Foods

Vibrio organisms concentrate in the gut of filter-feeding molluscan shellfish such as oysters,

clams, and mussels where they multiply and cohere. Although thorough cooking destroys these
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organisms, oysters are often eaten raw and are the most common food associated with Vibrio

infection in the United States (57). However, there have been reports of V. parahaemolyticus

infections associated with other seafood, including crayfish, lobster, shrimp, and crab. One

such report was a case-controlled study of sporadic Vibrio infections in two coastal areas of

Louisiana and Texas conducted from 1992-1993, in which crayfish consumption was reported

by 5 of 10 persons affected with V. parahaemolyticus infection (16). Outbreaks of V.

parahaemolyticus gastroenteritis aboard two Caribbean cruise ships were reported in 1974 and

1975 (87). The outbreaks were most likely caused by contamination of cooked seafood by

seawater from the ships’ seawater fire systems. In 1972, an estimated 600 of 1,200 persons who

attended a shrimp feast in Louisiana became ill with V. parahaemolyticus gastroenteritis (13).

Samples of uncooked shrimp tested positive for the organism. Three outbreaks occurred in

Maryland in 1971 (32). Steamed crabs were implicated in two of the outbreaks after crosscontamination with live crabs. The third outbreak was associated with crabmeat that had

become contaminated before and during canning. Recently, sampling studies in the Adriatic

Sea demonstrated the presence of V. parahaemolyticus in fish, mussels and clams (10).

Consumption

The purpose of this segment is to delineate the factors concerning the consumption of raw

molluscan shellfish containing V. parahaemolyticus.

Frequency of Consumption and Amount of Raw Molluscan Shellfish Consumed

Intake data for molluscan shellfish are readily available from a number of governmental and

non-governmental sources. However, because raw shellfish is not a commonly consumed food

(~10- 20% of the population will consume shellfish raw at least once during a year), the data are

typically based on very few eaters reporting consumption. The USDA Continuing Survey of

Food Intake by Individuals (CFSII) (135) and the food frequency survey conducted by the

Market Research Corporation of America (MRCA) (36) suggest that raw oysters are consumed

on average approximately once every 6 weeks. The mean amount of raw oysters consumed at a

single serving is 110 grams, approximately one-half dozen raw large Eastern oysters (128). The

distribution of shellfish intake will be derived from food intake surveys, food frequency

surveys, and from reported landings of shellfish and industry estimates of the percentage of

shellfish consumed raw.

Population at Risk

Anyone who consumes shellfish raw is “at risk” for infection by V. parahaemolyticus. An FDA

telephone survey completed in 1993 and repeated in 1998 has shown that consumption of raw

shellfish is not uniformly distributed (90). A higher percentage of men consume raw oysters

than women (16% vs. 7%), and raw shellfish consumption is higher for those living along the

coastline of the United States than for those living inland (22% vs. 13%). The trends in raw

shellfish consumption, as evidenced in the 1998 FDA survey is toward lowered consumption of

raw shellfish. This may be the result of education efforts by the Agency concerning the risks

associated with the consumption of raw or undercooked protein foods, such as beef, chicken,

eggs, and shellfish. Paradoxically, raw shellfish consumption is highest among those with the
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highest education levels, and the trend toward reduction in raw shellfish consumption over the

last 5 years is smallest in this education group.

Oyster Landings Data

The time of year of consumption was considered in the risk assessment, as most infections

occur during warm months, that is, a person consuming the raw oysters in July is at higher risk

than the same person consuming the same amount in December. The location of harvest is also

important, with most landings of oysters occurring in the Gulf, particularly off the coast of

Louisiana.
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V. HAZARD CHARACTERIZATION/DOSE-RESPONSE

Hazard characterization describes the adverse effects on the host of a particular substance,

organism, or other entity. It may be a quantitative and/or qualitative evaluation of the nature of

these adverse effects. Dose-response, which is quantitative, is the relationship of the levels of

V. parahaemolyticus ingested with the frequency and magnitude of illness. Human doseresponse relationships for V. parahaemolyticus can be derived directly from human clinical

feeding trials and epidemiological (outbreak) investigations, if sufficient data exist. To date,

little information in terms of estimated exposure doses has been obtained from outbreak

investigations. For V. parahaemolyticus, dose-response data is available directly from several

human clinical feeding trials. However, these studies were performed prior to 1974 with

uncharacterized strains, antacid administration and with no information on the immune status of

the volunteers in terms of preexposure to V. parahaemolyticus. Even partial immunity to V.

parahaemolyticus could raise the observable infectious dose compared to what may occur in the

general population. In addition, in outbreak settings, lower doses of V. parahaemolyticus may

cause illness if the organism is mixed with food that can buffer the gastric acidity thereby

lowering the infectious dose. It is unlikely that any additional human feeding studies with V.

parahaemolyticus will be undertaken due to the observed cardiotoxicity of TDH in animal

models (60, 119). In the absence of additional data for V. parahaemolyticus, an alternative for

dose-response modeling is to select an appropriate surrogate bacteria for which additional doseresponse data is either available or can be generated. Additional information considered

essential would include greater low dose exposure data (including biomarkers) and the role of

the food matrix on dose-response relationships. Information on non-O1 V. cholerae is provided

as a possible surrogate organism.

Since limited human clinical information was available, it was assumed that all V.

parahaemolyticus clinical isolates are equally virulent (105) and that the primary virulence

factor is TDH (105). This may be modified as new data become available that identify new

virulence determinants. In particular, recent data from British Columbia (77) may suggest an

association of urease positive strains with clinical isolates, which may or may not be TDH

positive.

Animal models using V. parahaemolyticus or a surrogate organism can be used as surrogates to

provide a basis for extrapolating dose-response estimates for humans. Animal models can also

be used to assess the virulence potential of different strains and serotypes, susceptibility of the

sensitive subpopulation (i.e., immune compromised), and to study the role of specific virulence

determinants. Several V. parahaemolyticus animal models have shown the virulence potential

of TDH negative strains (59,84). However, it remains to be determined whether the virulence

potential indicated for TDH- strains also applies to humans. The effect of food matrices and

other environmental factors on virulence and the dose-response relationship can be evaluated

more readily in animal models.
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Human Clinical Feeding Studies

Feeding trials with V. parahaemolyticus

In the study by Takikawa et al. (125), a Kanagawa-positive strain (production of TDH, as

observed on a blood agar plate) caused diarrhea in 1 of 2 individuals fed a dose of

approximately 106 cells. Diarrhea occurred in 2 of 2 individuals fed approximately 107 cells

(125). Ingested doses were estimated assuming that V. parahaemolyticus cultures can reach

maximum growth densities of approximately 1010 cells per milliliter.

Three Kanagawa negative strains (no production of TDH as observed on a blood agar plate)

isolated from cases of gastroenteritis were fed to groups of four volunteers each. No illness was

observed in any of the groups at doses as high as 2 x 1010 cells. A Kanagawa positive strain

also isolated from a gastroenteritis case produced no symptoms at a low dose of 200 viable

cells. Abdominal discomfort occurred in 1 of 4 volunteers at a dose of 2 x 105 viable cells, and

2 of 4 volunteers experienced abdominal discomfort and diarrhea at 3 x 107 viable cells. All

volunteers received antacid tablets prior to challenge with cultures suspended in gelatin (116).

Feeding tests carried out with 15 Kanagawa negative strains isolated from fish produced no

illnesses when doses as high as 109 viable cells were used (115).

Although never published, a personal communication is cited (71) that reports it took 6 to 8

hours incubation for a V. parahaemolyticus hemolytic variant to cause disease while an non

hemolytic variant required approximately 18 hours to cause disease after challenge. The

infecting dose was stated to be approximately 106 organisms. No information is provided about

the strain and it is unknown if the strain was truly a TDH- strain since no genetic analysis of the

strain was performed.

Feeding trials with non-O1 V. cholerae

One of three strains of non-O1 V. cholerae fed to healthy volunteers caused no diarrhea in 2

volunteers fed 105 cells, 2 of 3 fed 106, 1 of 2 fed 107 and 3 of 3 fed 109. Two other strains

produced no disease at doses as high as 109 cells(98).

V. cholerae O139 Bengal fed to volunteers caused diarrhea in 2 of 4 fed 104 cells and in 7 of 9

fed 106 cells (97).

Animal Models

For possible inclusion in future modeling, animal dose-response data and several factors

influencing the infectious dose of V. parahaemolyticus, using animal studies are described in

this section.
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Animal models for V. parahaemolyticus

Suckling rabbits infected orally with a Kanagawa-positive strain at doses of 109 to 1010 had

positive blood cultures in 9 of 36 tested, positive spleen cultures in 11 of 21 tested and positive

liver cultures in 14 of 21 tested (20). Similar doses of a Kanagawa negative crab isolate were

negative for bacteremia, liver or spleen invasion in all 12 animals challenged (20).

Combined results of seven experiments in which mice were challenged intraperitoneally with 1

of 7 strains (four TDH+ strains and three TDH- negative strains) resulted in 0 deaths with a dose

of 105 cells; 4% deaths with a dose of 106; 61% deaths with a dose of 107, and 90% deaths with

a dose of 108 cells (59).

Combined results of two experiments in which mice were challenged orally with 1 of 2 TDH+

strains resulted in 38% deaths with a dose of 107 cells, 57% deaths with a dose of 108 and 80%

deaths with a dose of 109 cells (59). There were no significant differences in mortality between

the TDH+ and TDH- strains at any of the doses.

In rabbit ileal loop studies the effective dose required to produce ileal loop dilation in 50% of

rabbits for three Kanagawa positive strains ranged from 2.6 x 105 to 7.7 x 106 cells. It was

estimated that the initiation of positive loops occurred with doses from 102 to 105 cells (134).

Animal models for other Vibrio spp.

Severity of disease increased and time until death decreased in rabbits when a non-O1 V.

cholerae strain shown to cause diarrhea in volunteers, was administered in increasing doses of

103, 104 and 109 cells using the removable intestinal tie adult rabbit diarrhea (RITARD) model

(114).

Fluid accumulation, diarrhea, and mortality of strains of non-O1 and O1 V. cholerae and V.

fluvialis was studied in orogastrically challenged suckling mice. The 50% lethal dose values

ranged from 107 to 109 CFU. The effective oral dose producing stained feces was about 1 log

lower than the LD50 dose for each strain (106).

Factors Influencing the Infectious Dose of V. parahaemolyticus

ÿ Bacterial/Virulence Factors

• The percentage of positive blood cultures in orally challenged suckling rabbits

increases when strains that had been previously passed through suckling rabbits

are used (20).
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ÿ Host Factors

• Production of the TDH is enhanced by the presence of bile acids (112).

• Iron-limiting conditions enhance the virulence of V. parahaemolyticus in mice

after intraperitoneal challenge (33).

• Acid adaptation enhances the virulence of V. parahaemolyticus in mice after oral

challenge (144).

ÿ Food/Environmental Factors

• Studies with V. cholerae O1 indicate that a food matrix, such as cooked rice,

provides buffering capacity and may have substantive impact on dose-response

relationships (88).

• Addition of 5% mucin to the inoculum enhances the virulence of V.

parahaemolyticus in mice following intraperitoneal challenge (59).

Modeling of the Public Health Module

The Public Health Module predicts distributions of illness based on the distribution of serving

size (oysters per serving and weight of oysters), the predicted levels of pathogenic V.

parahaemolyticus at time of consumption and an estimated relationship between the probability

of illness and ingested number of organisms per serving (Fig. V-1). For the purpose of

modeling, only Kanagawa-positive V. parahaemolyticus strains have been considered as being

pathogenic. In most studies, greater that 90% of the V. parahaemolyticus strains isolated from

the stools of symptomatic cases are found to be Kanagawa-positive (34, 145). In contrast, less

than 1% of strains isolated from the environment are found to be Kanagawa-positive. To date

no other virulence determinant has been correlated with clinical disease. The predicted density

of pathogenic V. parahaemolyticus at time of consumption is determined in the Post Harvest

Module under assumptions of current industry practice and possible mitigations. Distributions

of number of organisms ingested are obtained by multiplying the estimated densities of

pathogenic V. parahaemolyticus by serving size.

Weight per oyster (g)

Density ( V ppath /g

# oysters per serving

Ingested Dose

D 1, ...,D n

Probability and 

severity of illnessP(D 1

), ...,P(D n)

Figure V-1. Schematic depiction of the Public Health Module of the V. parahaemolyticus

(Vp) risk assessment model.
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Human clinical trials with Kanagawa-positive V. parahaemolyticus strains conducted prior to

1974 were evaluated for the purpose of determining a dose-response relation for converting

distributions of ingested doses into distributions of risk per serving (2, 116, 125). However, in

consideration of oyster consumption statistics and predicted levels of pathogenic V.

parahaemolyticus at time of consumption, the dose-response (e.g. ID50) in the human feeding

trials was found to overpredict the CDC estimates of annual number of illnesses by a factor of

10. This strongly suggests that the dose-response under conditions of normal population

exposure is different than under the conditions of the feeding trials. Possible reasons for the

difference include food matrix or immunological effects of preexposure to the organism

including antibodies/vaccines to the organism (88). Consequently, a plausible dose-response

was obtained by shifting the dose-response by a factor of 10, estimated from the feeding trials

so as to be generally consistent with CDC estimates of illness. The predicted number of

illnesses associated with oysters from each region and season were derived based on the

projected number of raw oyster-servings. The probable number of illnesses associated with the

oysters landed from each region was determined rather than the number of illness within each

geographic region. Obviously, the number of illnesses occurring within a given region and

season is due to oysters originating from various regions of the country.

An estimate of the distribution of risk per serving due to oysters originating from various

locations would be obtained as a weighted average of the distributions of risk associated with

oysters harvested from each region. However, a reliable estimate of the extent of interregional

transport, which would be necessary to estimate the appropriate weights, was not identified.

Distribution of dose of pathogenic V. parahaemolyticus per serving

The distribution of the dose of pathogenic V. parahaemolyticus ingested per serving was

estimated based on distributions of (a) the number of oysters consumed; (b) the weight of

oysters consumed; and (c) the density of pathogenic V. parahaemolyticus per g output from the

Post Harvest Module. The distribution of densities of pathogenic V. parahaemolyticus obtained

in the Post Harvest Module is projected to represent the variation of average density over

collections of oysters being consumed on any given occasion. This is appropriate in so far as

total V. parahaemolyticus densities measured in the DePaola et al. (38) study were average

densities over composites of twelve oysters and this is a typical serving size per serving.

Implicitly, a collection of oysters being consumed on any given occasion is assumed to have

originated from the same region.

The distribution of the number of oysters consumed per serving is shown in Figure V-2. The

most typical serving sizes were 6, 12 and 24 oysters. This frequency distribution is based on a

1994 Florida consumer survey conducted by the Florida Agricultural Market Research Center

(University of Florida) (36). To obtain the estimate of the distribution of meat weight per

serving, estimated distributions of meat weight for servings of a given size were combined with

the distribution of serving size.
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0%

5%

10%

15%

20%

25%

30%

35%

147

1013161922252831343740434649525558

Number of oysters

Frequency (percentage of population 

consuming a specific number of oysters)

Figure V-2. Observed frequency of number of oysters consumed per serving (University of

Florida consumption survey) (36).

Based on review of available data, the distribution of the meat weight corresponding to a

serving of n oysters was adequately approximated as a normal distribution with mean and

variance of n*μ and n*σ

2

, respectively, where μ and σ

2

 are the mean and variance of meat

weight per single oyster. The distribution was truncated to eliminate values below 15*n and

above 35*n grams on the assumption that it was unlikely that individual oysters would have

meat weight outside of the range of 15 to 35 grams. An estimate of the mean and standard

deviation of meat weight of individual Gulf oysters is 26 and 7.3 grams respectively (37).

When combined with the distribution of serving size, the resulting distribution of meat weight

per serving was considered typical of all regions.

Although, oysters harvested in the Pacific Northwest are somewhat larger than Gulf oysters the

meat weight per serving is unlikely to vary substantially across different regions of the country.

The distribution of number of oysters per serving is based on a survey of Florida consumers

who would have been consuming predominately Gulf oysters. Therefore it is appropriate to

combine this distribution with the distribution of meat weight of Gulf oysters.

The distribution of the number of pathogenic V. parahaemolyticus ingested per serving was

determined by multiplying the distribution of the densities of pathogenic V. parahaemolyticus

per gram oyster meat (the average density projected for composites of oysters) and the

distribution of meat weight per serving, as determined above.
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Number of raw oyster servings

The number of raw oyster servings associated with oysters harvested from different regions and

seasons was estimated based on National Marine Fisheries Service (NMFS) landings data. The

total monthly oyster landings reported by NMFS were averaged over the period 1990 to 1998,

then grouped by season and region (Table V-1). Oyster landings are reported by NMFS as

pounds of oyster meat weight. Industry figures suggest that 50% of the harvest is consumed

raw with little variation across different seasons. Therefore, the projected number of illnesses

which may occur have been based on the assumption that 50% of the harvest is consumed raw.

The actual fraction of the landings consumed raw in any particular year may vary somewhat

with a range of 40% to 60% being reasonable. However, total landings also vary from year-toyear in a manner that is not predictable due to the influence of other factors (e.g. closures due to

water quality, effect of parasites). Oyster landings have been generally increasing over the past

5 years but it is uncertain to what extent this trend will continue in the future.

Table V-1. National Marine Fisheries Service (NMFS) average yearly oyster landings

1990-1998

Average Number of Pounds of Oyster Meats Harvested

Location Winter

(Jan - March)

Spring

(April - June)

Summer

(July - Sept)

Fall

(Oct - Dec)

Total

 Atlantic

Northeast 2,112,000 714,000 676,000 3,710,000 7,212,000

Mid-Atlantic 946,000 125,000 66,000 1,492,000 2,629,000

Gulf Coast

 Louisiana

 Other States

 Gulf Total

2,751,000

2,096,000

4,848,000

2,630,000

1,393,000

4,023,000

2,854,000

847,000

3,701,000

2,769,000

2,358,000

5,127,000

11,004,000

6,694,000

17,699,000

Pacific

Northwest 2,402,000 1,682,000 1,379,000 3,181,000 8,644,000

Total 10,308,000 6,544,000 5,822,000 13,509,000 36,183,000

Source of data: http://www.nmfs.noaa.gov/
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The number of raw servings associated with oysters from each harvest region and season were

estimated as:

W S

L f

i

*

*

where Li are the regional and seasonal total landings expressed in units of meat weight (grams),

f is the percentage of oysters consumed raw, W is the average meat weight per oyster (grams),

and S is the average number of oysters per serving (14.7 based the 1994 FL consumer survey)

(36).

Dose-Response

Generally, the most appropriate data upon which to estimate the dose-response relationship of a

bacterial pathogen would be the outcome of feeding trials with human subjects. Although

subjects selected for feeding trials tend to be healthier than the general population, the

magnitude of the uncertainty when extrapolating from such data is generally less than that

associated with the extrapolation of dose-response determined from animal studies. In

particular, animal data were not utilized in our initial modeling efforts because the endpoint was

death rather than illness. Measures of the severity of illness used in animal studies often do not

correspond with definitions of human illness on which reporting statistics are based.

 For pathogenic V. parahaemolyticus, as identified by the Kanagawa test, several human clinical

feeding trials were conducted prior to 1974. Epidemiological investigations of V.

parahaemolyticus provide additional information on plausible dose-response of gastroenteritis

but are somewhat limited due to the lack of data concerning ingested doses of pathogenic strains

associated with reported cases of illness (e.g. epidemiological traceback studies).

Epidemiological case series data do provide valuable information which can be used to estimate

the likelihood of illness progressing to more severe outcomes (i.e., septicemia, death) for both

immune compromised and otherwise healthy populations. Estimates pertaining to the severity

of illness based on epidemiological data are presented in the next section.

The dose-response relationship for illness (gastroenteritis or septicemia) was estimated based on

the 1974 study of Sanyal and Sen (116) augmented by data from studies by Takikawa (125) and

Aiso (2). Overall, 5 of the 16 subjects who received higher ingested doses of Kanagawa

positive strains developed symptoms of gastroenteritis in these studies. No severe outcomes

were observed. Dose-response, using all the doses from the three studies, was characterized by

fitting several dose-response models chosen to span the range of extremes of model extrapolated

risks at projected levels of population exposure.

The selected models were the Beta-Poisson, Gompertz, and Probit (Log-normal). The

Gompertz and the Probit are generalized linear models. For these two models, the linear

predictor was chosen to be a linear function of log10 ingested dose. The mathematical form of

these dose-response models is shown in Table V-2.
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 Table V-2. Dose-response models of the relationship between probability of illness and

number of V. parahaemolyticus organisms ingested.

Dose-response

model

Risk of illness as a function of dose a

Beta-Poisson a

b

−

Pr( | ) = 1− (1+ )

d

ill d

Probit Pr( | ) ( *log ( )) ill d = Φ a+ b 10 d

Gompertz Pr( | ) 1 exp[ exp[ *log ( )]] ill d = − − a+ b 10 d

a α and β are the location and shape (steepness) parameters, respectively, for the Probit and Gompertz

models; α and β are the shape (steepness) and location parameters, respectively, for the Beta-Poisson; Φ

denotes the cumulative distribution function of a standard normal random variable

The maximum likelihood estimates (MLE) of the Beta-Poisson, Gompertz and Probit doseresponse models are shown in Figure V-3. For example, at a dose of 100 (2 log10) V.

parahaemolyticus organisms, the Beta-Poisson model predicts a risk of approximately 7 cases

of illness per 10,000 challenges at that dose. Best estimates of the risk of illness at ingested

doses of less than 103 organisms vary by more than 10-fold across this set of plausible models.

However, based on the estimates of exposure developed in the Harvest and Post Harvest

Modules, the mean (average) exposure to pathogenic V. parahaemolyticus exceeds 100,000

cells per serving for the Gulf Coast summer harvest. At this level of exposure the differences

between the dose-response models is not substantial. The average dose associated with the Gulf

Coast summer harvest is less than 2 log10 below that of ID50 estimates for the human feeding

trial studies based on any of the three models considered.

Consideration of the predicted density of pathogenic V. parahaemolyticus, the number of raw

oyster servings for the Gulf Coast summer harvest and the likely number of illnesses occurring

(CDC personal communication) (80), strongly suggests that the predicted risks per serving

based on dose-response curves shown in Figure V-3 are not plausible. Consequently, direct

extrapolation of the dose-response under conditions of exposure in the feeding trials is not

supported by the epidemiological data. The human feeding trials were conducted under

conditions of concurrent antacid administration. For V. cholerae, the ID50 observed in feeding

trials is known to be substantially lower when V. cholerae is ingested with antacid versus no

antacid (88). The same effect is likely to be the case with V. parahaemolyticus. It is also

possible that food matrix or immunological effects of preexposure to the organism, including

antibodies/vaccines, contribute to the apparent difference in dose-response obtained under

experimental versus natural conditions (88). Antibodies to V. cholerae increased the ID50 (88).

Likewise, prior consumption of oysters containing non pathogenic V. parahaemolyticus, may

also contribute to a higher ID50. It has also been reported, for example, that in rats, high milk fat

intake results in higher concentrations of gastric bactericidal lipids, which protect against

Listeria infection (123). Furthermore, gastrin, the most potent stimulant of gastric acid

secretion is released after eating a protein-rich meal, like oysters (8). The increased production
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of gastric acid would provide greater protection against infection, thus increasing the infectious

dose.

The relevant epidemiological data is summarized by estimates of annual incidence of Vibrio

illness developed by Mead et al. (94) as part of a comprehensive evaluation of the national

burden of infectious food-related illness in the United States. Mead et al. (94) have estimated

an average annual burden of 7,880 Vibrio illnesses excluding those due to V. vulnificus, and that

65% percent of this total incidence is estimated to be food-related. This estimate was based on

frequency of reported cases obtained by passive surveillance from 1988 through 1996 and

frequency of reported cases through FoodNet in 1996 as extrapolated to the 1997 population.

This total illness caused by non- vulnificus Vibrio spp. is based on an estimate of 20 to 1

underreporting and underdiagnosing of illness (80, 94).

The reported cases of illness attributable to V. parahaemolyticus in recent years is a component

of the data on which total incidence of non- vulnificus Vibrio illness is based on the information

reported by Mead et al. (94). Specific yearly estimates of total illness attributed to V.

parahaemolyticus for 1996, 1997 and 1998 are 4128, 15088 and 8567, respectively (94), based

upon active FoodNet data surveillance. Assuming that 65% of these illnesses are food-related,

CDC estimates that the total number of foodborne V. parahaemolyticus cases in the United

States for 1996, 1997 and 1998 was approximately 2700, 9800, and 5600, respectively.

Given these estimates of annual illness rate it was determined that at least a 10-fold increase of

the ID50 estimated with respect to the feeding trials was necessary to infer a dose-response

consistent with the epidemiology. It is possible that the true ID50 for the general population is

even greater than implied by this adjustment but this uncertainty was not evaluated in the

present risk assessment.
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0.00001

0.0001

0.001

0.01

0.1

1

0 2 4 6 8 10 12

log10 dose of Vp

Probability of Gastroenteritis

Sanyal et al 1974 (117)

Aiso 1963 (2)

Takikawa 1958 (126)

Beta-Poisson

Probit

Gompertz

Figure V-3. Maximum likelihood estimates (MLE) of the Beta-Poisson, Gompertz, and

Probit dose-response curves based on pooled data from human feeding studies

of V. parahaemolyticus (Vp).

The uncertainty with regard to which model is most appropriate for risk extrapolation is often

referred to as a structural uncertainty. An additional source of uncertainty in dose-response is

referred to as parameter or statistical uncertainty. This uncertainty derives from the estimation

of the parameters of a given model based on a small sample of observations. Parameter

uncertainty is present even if there is no structural uncertainty.

Parameter uncertainty in the extrapolated risk for the Beta-Poisson, Gompertz and Probit

models was evaluated by nonparametric bootstrapping (i.e., replication) of feeding trial

outcomes. Using this procedure, for each possible bootstrap, the models were refit to obtain a

distribution of parameter estimates corresponding to the possible (unrealized) outcomes of the

human feeding trials. The series of parameter estimates obtained are weighted by the

probability of the corresponding bootstrap outcomes. For each model, the distribution of

parameter estimates obtained defines the uncertainty of predictions or extrapolations conditional

on the structure of the model (i.e., in the absence of structural uncertainty). For example, the

parameter uncertainty associated with the model predicted infectious dose level corresponding

to a risk of 10-3 is shown in Figure V-4 (as estimated by 1,000 bootstrap samples) for each of

the three models considered. As can be seen, the uncertainty distribution of this particular

benchmark dose has somewhat the same spread (or range) for both the Gompertz and the Probit.

However, since the distribution for the Gompertz places much more weight on the extremes of

the range, the uncertainty of the estimate is much greater for the Gompertz than for the Probit
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model. The uncertainty is somewhat less for the Beta-Poisson. The means of all three of these

distributions are comparable to the predictions of the MLEs for these models, as shown in

Figure V-4.

0%

10%

20%

30%

40%

50%

0 1 2 3 4 5 6 7

log10 dose

frequency of dose corresponding to 10-3 risk 

Beta Poisson

Probit

Gompertz

Figure V-4. Uncertainty distribution of infectious dose of V. parahaemolyticus

corresponding to 10-3 risk for Beta-Poisson, Gompertz, and Probit doseresponse models.

Parameter uncertainty was evaluated in the risk assessment by simulating the uncertainty of the

estimate of risk associated with any dose consumed. For each of the models considered, the risk

of illness at a specific dose was considered to have a distribution determined by the bootstrap

distribution of parameter estimates. Given a risk of illness on an eating occasion/serving,

whether or not illness occurs was then modeled as a Bernoulli random trial with the

corresponding risk of illness as parameter. Structural uncertainty was evaluated by conducting

multiple simulations using the different structural forms for the dose-response extrapolation.

Severity of Illness

The output of the Public Health model is the probability distribution for the number of illnesses

expected to occur from consumption of oysters from different sources (seasons/regions). These

predictions assume that the probability of infection at any given ingested dose is the same for all

consumers. Clearly, most infections go unreported. In the Gulf Coast states where V.

parahaemolyticus infections are more actively identified, individuals who are immune

compromised or have liver disease are notably over represented in the case series of cultureconfirmed illness. There are two possible explanations for this observation. It is possible that
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there exists a sensitive subpopulation that is more susceptible to infection at any given dose.

However, it is more likely that there exists a sensitive subpopulation which, given the

occurrence of infection, is more likely to progress to severe outcomes requiring the attendance

of a physician. That is to say, the overrepresentation of immunocompromised individuals in

culture-confirmed case series is likely to be a reporting phenomenon driven by the severity of

illness.

For the purpose of the risk assessment, we have assumed that there is no sensitive subpopulation

with respect to the occurrence of an infection leading to gastroenteritis or septicemia. However,

given the occurrence of illness, we assume that it is more likely that the infection leads to severe

outcome (e.g. septicemia or death) among individuals with an underlying condition. The best

available information to quantify this differential likelihood of severe outcome is the CDC

database of culture-confirmed V. parahaemolyticus cases in the Gulf Coast states.

Estimates of the conditional probabilities of septicemia and death following infection leading to

illness in healthy and immune-compromised individuals can be estimated by using Bayes

theorem(46) and the frequency of underlying conditions among identified culture-confirmed V.

parahaemolyticus cases. Specifically, the calculation uses Bayes theorem in the form:

Pr( )

Pr( | ) * Pr( )

Pr( | )

condtion

condition outcome outcome

outcome condition =

where, for example, Pr(outcome|condition) denotes the probability or frequency of an outcome

among a population of individuals grouped by health status (condition). All factors on the right

hand side of the equation are identifiable from the epidemiological data.

The frequency of underlying conditions was identified among 107 oyster-related cultureconfirmed V. parahaemolyticus cases (sporadic- and outbreak-related) occurring during 1997

and 1998 in the Gulf Coast States (6). The statistics of the case series were:

• 5 septicemia

• 1 death

Of cases with available information:

ÿ 23 of 79 (29%) cases occurred in individuals with underlying chronic conditions

ÿ 27 of 90 (30%) gastroenteritis cases were hospitalized

ÿ 3 of 4 (75%) septicemia cases had an underlying chronic condition

Substituting the appropriate observed frequencies into the above equation provides estimates of

the probabilities of progression to more severe outcomes conditional on culture-confirmed

illness. For example, the probability of septicemia occurring following culture-confirmed

illness among individuals with underlying chronic conditions is estimated as follows:
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0.12

79

23

107

5 *

4

3

Pr( )

Pr( | ) * Pr( )

Pr( | )

= =

=

sensitive

sensitive septicemia septicema septicemia sensitive

The probability of septicemia occurring after culture-confirmed illness in healthy individuals is

estimated in a similar fashion. Overall, the estimated conditional probabilities of severe

outcomes based on the CDC data are:

Pr(septicemia |sensitive& culture confirmed ) = 0.12

Pr(septicemia |nonsensitive & culture confirmed ) = 0.0165

Pr(septicemia | culture confirmed ) = 0.047

Pr(death | septicemia) = 0.2

These estimated frequencies pertain to the population of culture-confirmed illnesses that may

occur in any given year. Certain assumptions are necessary to estimate the frequency of severe

outcomes occurring among the population of all V. parahaemolyticus illness, regardless as to

whether it is culture-confirmed or not.

Clearly, there is a selection bias towards more severe outcomes in the culture-confirmed case

series. It is unlikely that a significant fraction of cases of septicemia would go undiagnosed.

Overall, considering the less severe outcomes, it is estimated that 1 in 20 cases (5%) of V.

parahaemolyticus illness are reported or diagnosed in the Gulf Coast states (CDC, personal

communication) (6). Thus we would estimate approximately 2140 illness occurring over a time

period during which 5 septicemia cases were identified. Assuming that all septicemia are

culture confirmed, the Bayes calculation for the probability of progression to septicemia among

sensitive individuals who have become ill is:

0.006

79

23

2140

* 5

4

3

Pr(septicemia | sensitive & ill) = =

If only 50% of septicemia are reported and culture-confirmed, the corresponding estimate is

0.012. For healthy individuals, the estimated rates of septicemia following illness are 0.0008

and 0.0016, assuming complete and 50% underreporting, respectively.

Given estimates of conditional probabilities, the frequency of septicemia can be simulated in the

model based on the relative frequency of consumption of raw oysters by sensitive and healthy

individuals. Approximately 7% of the general population have an underlying condition

predisposing to V. vulnificus infection (82). The same set of conditions would likely predispose

to more severe V. parahaemolyticus illness. If sensitive individuals consume raw oysters at the

same frequency as the general population then the overall risk of septicemia occurring is the

weighted average of the conditional probabilities of septicemia for sensitive and healthy

individuals.
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0.93* Pr( | , )

Pr( | ) 0.07* Pr( | , )

septecemia ill healthy

septicemia ill = septicemia ill sensitive +

The distribution of the probable number of septicemia which may occur in a given year is

therefore a binomial with size parameter equal to total number of illnesses and the probability

parameter equal to the overall risk of septicemia following illness. Implicitly, this probability of

septicemia (and the probability of other severe outcomes) has been assumed to be independent

of the dose leading to infection and illness.
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VI. RISK CHARACTERIZATION

Risk characterization is the integration of the Exposure and Dose-Response assessments (Figure

VI-1). This phase describes the probability of illness caused by consumption of oysters

harboring pathogenic V. parahaemolyticus, and discusses the impact of the risk assessment. In

this section, the predicted number of illnesses associated with each region and season are

presented based on the model assumptions discussed in the previous sections. The distributions

of the probable number of illnesses that may occur associated with regional/seasonal harvests,

presented here, were derived based on the projected number of occasions when raw oysters

were consumed.

Simulation Results

As stated previously, the overall structure of the risk simulation was divided into three modules:

Harvest, Post Harvest, and Public Health. The Harvest Module simulated the variation in total

and pathogenic V. parahaemolyticus densities as a function of underlying environmental

conditions. Analysis of the effects of salinity on the levels of pathogenic V. parahaemolyticus

in the Harvest Module, suggested that salinity was not an important variable. The salinity

component was removed and a model developed for V. parahaemolyticus growth based solely

on water temperature. The output of the Harvest Module was the distribution of total and

pathogenic V. parahaemolyticus densities in oysters at the time of harvest. The Post Harvest

Module simulated the effect of current oyster handling practices and the possible effects of

mitigations to derive predictions of the distribution of total and pathogenic V. parahaemolyticus

densities at time of consumption. The Public Health Module estimated the distribution of the

probable number of illness, which may be expected to occur within any given region and season

on the basis of the predicted distribution of pathogenic V. parahaemolyticus densities at time of

consumption. Throughout the simulation we utilized derived distributions of influential

parameters and relationships between parameters to obtain estimates of the distribution of

pathogenic V. parahaemolyticus densities at various stages along the pathway from harvest to

consumption. Figure VI-1 shows a schematic representation of all the parameters used in the

simulations for each module and how the output of each module becomes a parameter for the

following module.

The distribution of ingested dose of pathogenic V. parahaemolyticus per serving was calculated

by combining the distributions of meat weight per serving and distributions of density from the

Post Harvest model. This was accomplished by following the Monte Carlo method of

resampling from these input distributions and multiplying the sampled values to generate the

distribution of consumed doses. Simulated samples from the distribution of ingested doses were

then converted to a corresponding distribution of risk per serving. For each region and season,

the number of illnesses occurring in 100,000 servings was then simulated as a sequence of

independent, but not identically distributed Bernoulli random variables. Although larger

simulation sizes are possible, the need to run multiple simulations (e.g. different regions and

seasons) necessitated limiting the size of each simulation to a practical level. Predicted numbers
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of illnesses were determined by projecting the number of illnesses per 100,000 to the number of

servings estimated based on the NMFS landings statistics. The simulations were repeated 50

times to simulate the year-to-year variation in the number of illnesses which may be expected

due to the year-to-year variation in such parameters as the distributions of water temperatures

and percentage of total V. parahaemolyticus which are pathogenic. The website where the

worksheet showing the formulae and parameters used for the model can be found is in

Appendix II.

Weight per oyster (g)

path Vp/g (density)

 at consumption

 # oysters per

 serving

Ingested Dose

RISK OF ILLNESS

Pathogenic Vp/g

 at harvest

Air temperature

Cooldown time

Vp/g at cooldown

Storage time

Time to refrigeration

Vp/g at 1st refrigeration

Water temperature

total Vp/g

Regional, seasonal & yearly variation HARVEST

POST HARVEST

PUBLIC HEALTH

Figure VI-1. Schematic diagram of the V. parahaemolyticus (Vp) risk assessment model

showing integration of all the modules.

Probable distribution of illness associated with regional/seasonal oyster harvest

The distribution of the probable number of illnesses predicted by the Monte Carlo simulation

was somewhat sensitive to the choice of the dose-response model. The difference in model

predictions is shown in Figure VI-2 for the Gulf Coast (Louisiana) harvest during the summer.

These histograms of the distributions are approximations based on 50 repeated simulations with

randomly varying factors such as mean seasonal water temperature and percentage of total V.

parahaemolyticus which are pathogenic. As shown in Figure VI-2, the predictions of illness

under the Gompertz dose-response model are considerably more variable than under either the

Beta-Poisson or Probit dose-response models. Preliminary simulations had suggested that a 10-

fold shift of the ID50 (e.g. in regard to possible food matrix and immunological effects) would

be sufficient to make model predictions generally consistent with CDC estimates of annual

illness. However, the extent to which the estimate of the ID50 for the human feeding trials

underestimates the "true" population ID50 is not known. If, for example, the difference in ID50

in the feeding trials versus conditions of general exposure was 10,000-fold, then it would not be

the case that a similar shift of the ID50 would make model predictions generally consistent with
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CDC estimates regardless as to choice of dose-response model. For the present, the uncertainty

in the magnitude of the ID50 under conditions of general population exposure was not fully

evaluated.

0%

10%

20%

30%

40%

50%

60%

70%

80%

0

250050007500

10000125001500017500200002250025000

>25000

Number of Vp illnesses

Frequency (percentage of times model predicted 

illnesses in a specified range)

Beta-Poisson

Gompertz

Probit

Figure VI-2. Effect of structural uncertainty of dose-response on projected number of

illnesses associated with V. parahaemolyticus (Vp) consumption in the

Louisiana Gulf Coast, summer harvest).

For example, as shown in Figure VI-2, the expected number of illnesses (i.e., mean of the

distribution) associated with summer harvest in the Louisiana Gulf Coast, predicted by the

Gompertz, is approximately 3,300 which is slightly higher than the expected 2,400 cases

predicted by the Beta-Poisson. The expected number of illnesses predicted by the Probit model

is 270, which is considerably less than that of the other two models. The results indicate that a

10-fold increase of the ID50 was sufficient to make model predictions consistent with CDC

estimates for either the Gompertz or Beta-Poisson dose-response models. A larger shift of the

ID50 would be necessary to predict the same number of illnesses based on the Probit doseresponse. Also, as evident in the figure, there is considerable variation about the expected

values; particularly for the Gompertz model, which is more heavily skewed towards higher

values. This variation represents the "statistical" uncertainty of the dose-response parameters

under each model (i.e., distribution of bootstrap parameter estimates) as well as the effect of

other influential variability parameters, particularly the distribution of water temperature.

Figure VI-2 suggests that, congruent with Figure V-4, there is considerably more statistical

uncertainty associated with the Gompertz dose response than the other two dose-response

models.
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The distribution of probable number of illnesses predicted by the Beta-Poisson model for the

whole Gulf Coast, Pacific Northwest and Mid-Atlantic regions are shown in Figures VI-3

through VI-6. Clearly, the largest numbers of projected illnesses were attributed to the Gulf

Coast harvest. For the Gulf, the average number of illnesses projected to occur associated with

current levels of consumption is 25 during the winter; 1,200 during the spring; 3,000 during the

summer, and 400 during the fall. The spread of the distribution is approximately 2-fold. For

the Pacific, the average number of illnesses projected to occur is approximately 15 during the

spring and 50 during the summer. The variance of the projected number of illnesses for the

Pacific during the summer is comparable to that projected for the Gulf. This similarity is

primarily a consequence of the assumptions concerning the year-to-year variation of percentage

of total V. parahaemolyticus which are pathogenic. As discussed in the Harvest Module

section, it has been assumed that the extent of year-to-year variation of this parameter relative to

the mean is the same in the Pacific as in the Gulf.

Model-predicted illness associated with the Pacific Coast harvest during the spring and summer,

predictions are low relative to epidemiologically based estimates. From 1990 to 1996 an

average of 8 culture-confirmed cases were reported in Washington State during the summer

months (72). This statistic includes recent El Nino years that are not typical but it does not

include confirmed cases in other Pacific Coast states, or the outbreaks in 1997 and 1998.

Consequently, in a typical summer season at least 8 culture-confirmed cases are expected in the

Pacific Northwest. This corresponds to about 160 cases assuming that illnesses are cultureconfirmed at a rate of 5%. Consequently the average model-based prediction (50 illnesses) is 3-

fold lower than the estimate based on the epidemiology. With regard to this discrepancy, it is

possible that intertidal exposure of oysters to ambient air temperatures may have an appreciable

effect on total and pathogenic V. parahaemolyticus densities in the Pacific growing areas (56).

This phenomenon is not reflected in the present risk assessment where V. parahaemolyticus

densities at harvest are predicted based on water temperature alone. It is also possible that

pathogenic strains in the Pacific (urease positive) are somewhat more virulent than those strains

indigenous to the other regions of the country.

In comparison to the relatively large number of illness projected for the Gulf harvests, expected

number of illnesses projected for the Mid-Atlantic harvest are 10 during the spring and 12

during the summer. As indicated in Figure VI-6, the occurrence of 30 or more illnesses

associated with Mid-Atlantic summer harvest is predicted to be a relatively rare event.

Expected numbers of illness likely to occur due to current levels of consumption of Northeast

Atlantic oysters are estimated to be 12 during the spring, 30 during the summer, and 7 during

the fall. For the winter, the distribution of probable number of illnesses for Northeast Atlantic

oysters is below the resolution of the simulation based on simulation of 100,000 servings.

Taking into consideration the NMFS landings data (104), and then assuming 50% consumed

raw plus average serving size of 12, in the Northeast Atlantic region, many more oysters are

consumed in the fall (2.2 million oyster servings) vs. the spring (400,000 oyster servings).

Consequently, the predicted mean number of illnesses is more in the fall than in the spring for

this region in consideration of the number of servings estimated.

The low numbers of projected illnesses due to Northeast Atlantic and Mid-Atlantic oysters is

attributable to both the colder water temperatures and the relatively modest harvest from these
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regions during the warm summer months. Considering the extent of underreporting of illness

that is likely to occur for self-limiting gastroenteritis due to V. parahaemolyticus infection, these

low numbers of predicted illness appear to be generally consistent with the infrequency of

culture-confirmed illness associated with oysters harvested from these regions. Underreporting

and infrequency of culture-confirmed illness may be partly caused by the unfamiliarity of

consumers and healthcare practitioners with Vibrio- associated illnesses, and lack of expertise in

diagnosis and in laboratory detection (34).

0%

10%

20%

30%

40%

50%

60%

0

100020003000400050006000700080009000

10000

>10000

Number of Vp illnesses

Frequency ( percentage of times model 

predicted illnesses in a specified range)

Spring

Summer

Figure VI-3. Probable number of V. parahaemolyticus (Vp) illnesses associated with spring

and summer Gulf Coast harvests.
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0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0

100200300400500600700800900

1000

>1000

Number of Vp illnesses

Frequency (percentage of times model

 predicted illnesses in a specified range)

Winter

Fall

Figure VI-4. Probable number of V. parahaemolyticus (Vp) illnesses associated with fall

and winter Gulf Coast harvests.

0%

10%

20%

30%

40%

50%

60%

70%

0

100200300400500600700800900

1000

>1000

Number of Vp illnesses 

Frequency (percentage of times model predicted 

illnesses in a specific range)

Spring

Summer

Figure VI-5. Probable number of V. parahaemolyticus (Vp) illnesses associated with

spring and summer Pacific Coast harvest.
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0%

5%

10%

15%

20%

25%

30%

35%

0 5 10 15 20 25 30 35 40 45 50 >50

Number of Vp illnesses

Frequency (percentage of times model 

predicted illnesses in a specified range)

Spring

Summer

Figure VI-6. Probable number of V. parahaemolyticus (Vp) illnesses associated with

spring and summer Mid-Atlantic harvest.

Based on the assumptions outlined previously with regard to description of the Public Health

Module, the projected distribution of probable number of cases with septicemia which may

occur in any given year is shown in Figure VI-7. Most of these cases are predicted to be

associated with Gulf Coast oyster harvest with few cases due to Pacific Northwest harvest.

Although the most probable number of cases of septicemia per year associated with ingestion of

V. parahaemolyticus, is 4, the overall mean of the distribution is 6 cases per year, based on a

sample of 50 replicated simulations. As evident in the figure, the occurrence of 15 or more

cases of septicemia in a single year for the entire country is projected to be an infrequent event

(i.e., an event with probability less than 0.1).
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0%

5%

10%

15%

20%

25%

30%

0 2 4 6 8 10 12 14 16 18 20 >20

Number of cases

Frequency (percentage of times model 

predicted septicemia in a specified range)

Figure VI-7. Distribution of probable number of cases of V. parahaemolyticus-associated

cases of septicemia occurring per year (all seasons and regions).

Predicted effect of mitigation strategies on risk/probability of illness

The effect of three Post Harvest mitigations was evaluated in the simulation: (a) mild heat

treatment (5 min at 50o C), (b) freezing (-30o C), and (c) rapid cooling immediately following

harvest (e.g., aboard ship). As discussed in the Post Harvest Module, the effect of mild heat

treatment has been shown to reduce the density of V. parahaemolyticus to nondetectable levels

(at least a 4.5 log10 reduction) and freezing at –30o C has been shown to reduce the density by

approximately 2 logs.

All three potential mitigation strategies have a substantial effect on the distribution of probable

number of illnesses. The effect of these mitigations was evaluated under the assumption of the

Beta-Poisson dose-response model. For the Gulf Coast summer harvest (Figure VI-8), a shift in

the distribution of probable number of illnesses down from a mean of 3,000 illnesses to

approximately 240 illnesses is predicted under the mitigation of rapid cooling. The mean

number of illnesses projected to occur under the freezing mitigation is approximately 15. As

evident in Figure VI-8, the variance of the predicted distribution is also reduced under these

mitigations.

The effect of mild heat treatment was found to reduce the mean risk of illness per serving to

substantially less than 1 in 100,000. As discussed previously, practicalities of performing the

Monte Carlo simulation necessitated limiting the simulations to 100,000 servings per

season/region combination and then projecting to an estimated number of servings.

Consequently, the distribution of probable number of illnesses under mild heat treatment
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mitigation could not be accurately determined. For the Gulf Coast summer harvest the mean of

the distribution is certainly less than 10 illnesses.

With the exception of rapid cooling, the effect of these potential mitigations on the number of

illnesses is similar for other regions and seasons. The relative effectiveness of rapid cooling in

the Pacific Northwest is predicted to be much less than in the Gulf Coast or Mid-Atlantic

regions due to cooler air temperatures and a shorter duration of harvest. The effects of the

mitigations on the mean risk per serving are shown in Figures VI-9 through VI-11 for the

Pacific, Mid-Atlantic and Gulf Coast harvest for all seasons. As evident in these figures, the

effectiveness of mitigation is more pronounced during the summer since the potential for

growth is much greater during this time of the year.

0%

25%

50%

75%

100%

0

500

100015002000250030003500400045005000

>5000

Number of Vp Illnesses 

Frequency (percentage of times the model

predicted illnesses in a specified range)

no mitigation

heat

freeze

rapid cool

Figure VI-8. Effect of potential mitigations on the distribution of probable number of

illnesses associated with V. parahaemolyticus (Vp) in oysters harvested from the Gulf

Coast in the summer.
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0.0000

0.0004

0.0008

0.0012

0.0016

0.0020

Predicted mean risk per serving

Winter Spring Summer Fall

Figure VI-9. Effect of potential mitigations on the distribution mean risk of V.

parahaemolyticus illnesses per serving associated with Gulf Coast harvest.

No mitigation ( ̈); freezing (à); heat treatment (è); rapid cooling (Œ).

0.00000

0.00002

0.00004

0.00006

0.00008

0.00010

Predicted mean risk per serving

Winter Spring Summer Fall

Figure VI-10. Effect of potential mitigations on the distribution mean risk of V.

parahaemolyticus illnesses per serving associated with Pacific Coast harvest.

No mitigation ( ̈); freezing (à); heat treatment (è); rapid cooling (Œ).
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0.0000

0.0001

0.0002

0.0003

0.0004

Predicted mean risk per serving

Winter Spring Summer Fall

Figure VI-11. Effect of potential mitigations on the distribution mean risk of V.

parahaemolyticus illnesses per serving associated with Mid-Atlantic Coast harvest:

No mitigation ( ̈); freezing (à); heat treatment (è); rapid cooling (Œ).

Evaluation of the FDA guideline of 10,000 V. parahaemolyticus/g of shellfish

FDA had previously indicated that V. parahaemolyticus in shellfish should not exceed a level of

10,000 viable cells per gram (64). The 1999 V. parahaemolyticus Interim Control Plan adopted

by the ISSC includes the 10,000 viable cells per gram guidance. In areas where levels of greater

than 10,000 cells/g oyster tissue are found, the area would need to be resampled. While the

critical cause of illness in humans is the level of pathogenic V. parahaemolyticus, the total V.

parahaemolyticus is used as a convenient surrogate indicator of higher risk of illness. The risk

assessment cannot critically evaluate the control plan because, as the model is constructed, there

is no mechanism included to account for the possibility of persistence of V. parahaemolyticus in

specific oyster harvesting areas. Moreover, the rapidity or sensitivity of tests performed by

individual laboratories for detection of V. parahaemolyticus is not well determined. Both of

these factors are critical in evaluating the control plan.

The risk assessment does, however, allow us to ask what would be the predicted impact on the

incidence of disease if we were able to exclude oysters at the time of harvest that had a specified

level of V. parahaemolyticus. This also includes estimating the impact of what excluding

oysters that had a specified level of V. parahaemolyticus would have on the percentage of

oysters that would no longer be available. The impact of such a criterion was evaluated using

parameters from the Gulf summer (Louisiana) region/season parameters. A total of fifty

simulations of 33,333 iterations (Monte Carlo samples) were run individually.
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The results were sorted by whether or not they caused illness and then the initial V.

parahaemolyticus levels in the environment (i.e., at harvest) were sorted into "bins" of half log

intervals. The proportion of illness associated with each half-log interval of initial V.

parahaemolyticus level was calculated and the results were then used to estimate the potential

effect of various guidance levels for "at harvest" densities on reduction of illness and associated

cost in terms of percentage of total harvest lost (e.g., diverted from raw consumption market).

The results are shown in Figure VI-12 for the 10,000 per g standard, with levels of 100, 1,000

and 100,000/g included for comparison. An "at harvest" perspective was adopted here due to

the fact that the current guidance level of 10,000 viable cells per g included in the 1999 V.

parahaemolyticus Interim Control Plan pertains to oyster samples obtained at time of harvest.

Although the guidance level of 10,000 viable cells per g may apply to shellstock at any time

post harvest, current monitoring efforts are generally directed towards "at harvest" samples.

Consequently, the potential effects of different guidance levels for monitoring of shellstock at

the wholesale or retail level was not evaluated.

0%

20%

40%

60%

80%

100%

100,000 10,000 1,000 100

Total V. parahaemolyticus /g

Percentage of illness prevented

0%

20%

40%

60%

80%

100%

Percentage of oyster harvest lost

Lost oyster harvest

Illness

Figure VI-12. Potential effect of control of total V. parahaemolyticus per gram

at harvest (Louisiana Gulf Coast summer harvest).

On average, the simulation results suggest that 15% of the illnesses are associated with the

consumption of oysters for this region/season combination that contain greater than 4 log10 (104)

V. parahaemolyticus per g at time of harvest. The corresponding fraction of the harvest
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containing greater than 4 log10 (104) per g was 5%. Therefore, if all shellstock could be

evaluated for total V. parahaemolyticus at time of harvest, the simulation results suggest that

excluding all oysters that had levels of 10,000 viable cells per g would reduce (sporadic) illness

by 15% at a loss of 5% of the total harvest from the raw consumption market. This relatively

low (potential) reduction of illness is attributable to the large proportion of the harvest that

would remain with a lower, but still significant, associated level of risk. In comparison, the

simulation results suggest that in the absence of subsequent post harvest mitigations, "at

harvest" guidance levels of 5 log (105), 3 log (103) and 2 log (102) total V. parahaemolyticus per

g could (potentially) reduce the illness rate by 2%, 50% and 90% with corresponding losses of

0.3%, 25% and 70% of the harvest, respectively.

Sensitivity Analysis

A tornado plot is a convenient way of describing the factors in our model that most affect the

results. The plot is called a "tornado plot" because of the similarity of the image of a tornado

with the graphical arrangement of the factors from most influential at the top to least influential

at the bottom. In our model, we see that the most influential factor driving the results for all the

harvesting areas during the summer (Figures VI-13, VI-14, VI-15, VI-16) is the levels of V.

parahaemolyticus. Because our model assumes that the rate of increase of pathogenic V.

parahaemolyticus, is the same as that for total V. parahaemolyticus, then V. parahaemolyticusassociated illness results when the level of V. parahaemolyticus increases, thus increasing the

levels of pathogenic V. parahaemolyticus.

In the Gulf Coast, both Louisiana and the remaining Gulf Coast regions, where the temperatures

are the warmest compared to the other regions, time to refrigeration was determined by

sensitivity analysis to be the second most important effect on occurrence of illness (Figures VI13, VI-14).

The other factors analyzed have significant effects, but to a lesser extent. As one would expect,

the more oysters one eats, the more likely it is that one will become ill. Also, unsurprisingly,

conditions that allow for the V. parahaemolyticus to grow within the oyster (length of time

oysters are unrefrigerated, time it takes to cooldown the oysters, water and air temperature)

increase the risk of illness. Since the levels of V. parahaemolyticus decrease during cold

storage, the length of time the oysters are refrigerated is negatively correlated with the risk and

the factor points on the tornado plot in the opposite direction.
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-0.4 -0.2 0 0.2 0.4 0.6 0.8

Log Vp in the

environment

Time

unrefrigerated

Grams oysters

consumed

Water temperature

Duration of

cooldown

Ambient air temp

Length of refrigeration time

Figure VI-13. Tornado plot of influential parameters on log10 risk of V. parahaemolyticus

(Vp) illness per serving of raw oysters (Gulf Coast excluding Louisiana, summer oyster

harvest).

-0.4 -0.2 0 0.2 0.4 0.6 0.8

Log Vp in the

environment

Time

unrefrigerated

Grams oysters

consumed

Water temperature

Duration of

cooldown

Ambient air temp

Length of refrigeration time

Figure VI-14. Tornado plot of influential parameters on log10 risk of V. parahaemolyticus

(Vp) illness per serving of raw oysters (Louisiana Gulf Coast summer harvest).
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-0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Log Vp in the

environment

Water temperature

Grams oysters

consumed

Duration of

cooldown

Ambient air temp

Time

unrefrigerated

Length of refrigeration time

Figure VI-15. Tornado plot of influential parameters on log10 risk of V. parahaemolyticus

(Vp) illness per serving of raw oysters (Pacific Northwest Coast summer harvest).

-0.4 -0.2 0 0.2 0.4 0.6 0.8

Log Vp in the

environment

Water temperature

Grams oysters

consumed

Time

unrefrigerated

Ambient air temp

Duration of

cooldown

Length of refrigeration time

Figure VI-16. Tornado plot of influential parameters on log10 risk of V. parahaemolyticus

(Vp) illness per serving (Mid-Atlantic summer harvest).
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Model Validation

Reasonableness of model predictions and the appropriateness of the modeling assumptions that

have been used in the risk assessment, can be evaluated by comparing model output to relevant

data that were not used to develop the relationships and distributions of parameters in the model

per se. With regard to the prediction of illness there is no independent data available for this

purpose. As indicated previously, with due consideration of estimated levels of pathogenic V.

parahaemolyticus at time of consumption and oyster landing statistics, the dose-response under

conditions of feeding trials is not consistent with CDC estimates of annual illness.

Consequently, the epidemiological data were used to adjust the dose-response from the

conditions of human feeding trials to conditions of population exposure and do not constitute a

point of validation of the model. Independent data are available on the levels of total V.

parahaemolyticus at retail and these data have been compared to model predictions in order to

assess the appropriateness of the model with respect to the Harvest and Post Harvest Modules.

A collaborative nationwide survey of V. parahaemolyticus densities in oysters at the retail level

i.e., restaurants, oyster bars, wholesalers, etc., was conducted by the ISSC and FDA in 1998 and

1999 (44). A total of 370 oyster samples were collected during the study and the harvest state

was identified for all samples. This study provides the most comprehensive information

available on seasonal and regional differences in density of total V. parahaemolyticus at time of

consumption. In particular, this information provides a point of empirical validation of the

assumptions used in the Post Harvest Module to predict the extent of growth that occurs post

harvest. To facilitate this comparison, simulations of the distribution of total V.

parahaemolyticus densities were carried forward through the simulation in addition to the

distribution of pathogenic V. parahaemolyticus.

In the ISSC/FDA study, V. parahaemolyticus densities were enumerated by an MPN method. A

relatively high proportion of the non-Gulf Coast samples had nondetectable levels. To

appropriately adjust for the varying proportion of nondetectable V. parahaemolyticus across the

different regions and seasons, estimated means were obtained by fitting a Tobit regression to the

complete data set (n=370) with different harvest region and season combinations as a predictor

variable. The variance about the group means was assumed to be homogeneous i.e., the same

across different regions and seasons. The limit of detection varied somewhat from sample to

sample but was generally 0.18 MPN/g. Clearly, estimates of regional/seasonal means near or

below this threshold are an indication that a high proportion of samples from that particular

grouping were not detectable and the estimate of the mean is strongly influenced by the

assumptions underlying the Tobit model. In particular, estimates for the Pacific Coast were

poor due to lower levels of V. parahaemolyticus in that region year round and the low number

of samples obtained from the West Coast during the study.

Comparison of estimates of mean and population standard deviation of log10 total V.

parahaemolyticus densities based on the ISSC/FDA study versus model predictions are shown

in Figures VI-17 through VI-19 for the Gulf Coast, Pacific Northwest, and Mid-Atlantic harvest

regions. In so far as the mean and standard deviation of the log10 densities predicted by

simulation varies from year to year due to environmental conditions, model predictions are
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presented after averaging out year-to-year variation. A certain degree of deviation from

ISSC/FDA estimates is to be expected in this comparison since the empirical data were obtained

over the period of a single calendar year.

Generally, the estimates of the means based on ISSC/FDA data compared well with those

predicted by the simulations. In particular, model predictions of mean log10 densities are in

good agreement with ISSC/FDA data for both the Gulf and Mid-Atlantic regions during the

summer when the risk of illness is highest. For the Gulf Coast, model predictions of mean log10

densities in the fall are somewhat lower than those obtained by the ISSC/FDA study. With

regard to this discrepancy, water temperature measurements indicate that the fall season of

1998, corresponding to the time of ISSC/FDA sampling, was somewhat warmer than usual.

The NBDC buoy at Dauphin Island has been disabled since September 1998; however, the

average daily noontime water temperatures in nearby Weeks Bay AL (an NERR site) during the

fall of 1998 was 22.5o C, compared with a typical average of 18o C at Dauphin Island. A

difference of 4.5o C corresponds to an average of 0.50 log10 higher density of total V.

parahaemolyticus at time of harvest. Furthermore, warmer air temperatures would entail more

post harvest growth. From January 1999 through September 1999, corresponding to the

remaining period of ISSC/FDA sampling and the other seasonal comparisons, water

temperatures in Weeks Bay did not differ greatly from the overall averages measured at the

Dauphin Island buoy (i.e., ~1o C difference).

For the Pacific Northwest harvest, average model predictions were higher than the estimates

based on ISSC/FDA data. The difference in the summer appeared to be in the range of what can

be expected due to year-to-year fluctuations of environmental conditions. However, model

predictions during the spring are much higher than those based on the ISSC/FDA data. A

possible explanation for this discrepancy is the lack of precision associated with the estimate

based on ISSC/FDA data. The number of samples on which the estimate is based was very

small and consequently, due to the generally low levels of total V. parahaemolyticus in the

spring season, the estimate of the mean is poor. Estimates could not be obtained for the Pacific

winter or fall season. Similar results were obtained when considering Northeast Atlantic

harvest (data not shown). Model predictions of mean density were consistent with the

ISSC/FDA estimate for the summer but 1 to 1.5 log10 higher during other seasons of the year.

The common population standard deviation about regional and seasonal mean densities was

estimated to be 1.5 log10 based on the ISSC/FDA data. This compares well with model

predictions of the spread of the distribution when allowing for the fact that variance of

measurements obtained in the survey are also a reflection of method error which has been

adjusted for in the simulation. The error bars in Figures VI-15 through VI-17 denote one

standard deviation above and below the mean. The interval is generally larger for the

ISSC/FDA retail study data than for the simulation output.
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-1

1

3

5

Density (log Vp/g)

ISSC/FDA retail data

model predictions

Winter Spring Summer Fall

Figure VI-17. Observed retail level distribution of density of total V. parahaemolyticus

(Vp) compared to model predictions for all seasons (Gulf harvest) (28).

-3

-1

1

3

5

Density (log Vp/g)

ISSC/FDA retail data

model predictions

Winter Spring Summer Fall

Figure VI-18. Observed retail level distribution of density of total V. parahaemolyticus

(Vp) compared to model predictions for all seasons (Mid-Atlantic harvest) (28).
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-5

-3

-1

1

3

Density (log Vp/g)

ISSC/FDA retail data

model predictions

Winter Spring Summer Fall

Figure VI-19. Observed retail level distribution of density of total V. parahaemolyticus

(Vp) compared to model predictions for all seasons (Pacific harvest) (44).

The value of information which could be obtained by additional studies

Additional simulations were performed to examine the effect of uncertainty and variability

parameters on the variance of the distribution of probable number of illnesses obtained by

simulation. These simulations were directed towards determining the influence of three

parameters: (a) relative growth rate of V. parahaemolyticus in oysters versus broth model

(axenic rate); (b) combination of variability and uncertainty in the overall percentage of total V.

parahaemolyticus that are pathogenic (% pathogenic); and (c) variation of water temperature.

The average risk per serving for Gulf Coast summer harvest under a series of five alternative

assumptions is shown in Table VI-1. Fifty repeated simulations were performed under each set

of assumptions. In two of the series, the parameters were either varied according to their

distributions as specified in the description of the risk assessment or they were held constant at

their mean values ("vary all" and "hold all"). In the remaining three series of simulations one

parameter was held fixed while the other parameters varied according to their specified

distributions.
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Table VI-1. Effect of selected uncertainty and variability parameters with respect

to Gulf Coast summer harvest: average and relative variation of predicted risk per serving

for the V. parahaemolyticus risk assessment model

Vary allb Hold axenic

(4.0)

Hold % Vppath

(0.2%)

Hold temp

(m=28.9,

s=1.5)

Vary None

(hold all)

Average risk

per serving 0.00134 0.00150 0.00160 0.00163 0.00162

Coefficient of

variation a 98.0% 72.2% 86.0% 75.9% 53.7%

a

 standard deviation divided by the mean

b

 baseline (vary all parameters), no growth rate uncertainty (hold axenic), average percentage pathogenic

known (hold % path), no temperature variation (hold temp), no uncertainty/variation in all three

parameters (vary none)

Due to differences in means for the five series of assumptions, the variation in the model output

(risk per serving) is summarized in Table VI-1 by the coefficient of variation (the standard

deviation of a distribution divided by its mean). A clear difference is evident between the

distribution of risk per serving obtained when all parameters were varied versus that when all

parameters are held fixed. The coefficient of variation is 54% when all three parameters are

held fixed and 98% when all parameters are varied according to their specified distributions.

Consequently, the three parameters considered here account for approximately 45% of the total

variation in risk per serving associated with the Gulf summer harvest. Individually, the

uncertainty in growth rate proportionality and percentage pathogenic account for 26% and 12%

of the total variation, respectively. In comparison, water temperature, which is a variability

parameter, accounts for 22% of the total variation. These results suggest that, of the uncertainty

factors considered here, the variation in the output would be reduced the most by determining

the appropriate proportionality constant between growth rate in oysters versus axenic culture

("hold axenic").

 An additional and important source of uncertainty underlying the predicted distribution of

illness is that associated with the dose-response extrapolation from frequency of illness in

feeding trial studies to conditions of human exposure. The effect of this uncertainty has not

been fully evaluated in the present risk assessment. Based on consideration of CDC illness

estimates, a plausible shift in the ID50 was determined for which model predictions of illness

were consistent with the CDC estimates. A more complete evaluation of this uncertainty could

be examined as a refinement of the present risk assessment.

Comments on the model

This model incorporates similar components of other risk assessments, but has several unique

aspects. This model has analyzed risk in terms of region and season. Other microbial risk

assessments have only looked at aggregate yearly risk. This model is scalable in that it may be

applied to finer levels of analysis as data become available. Other microbial risk assessment

models must be restructured to incorporate finer levels of analyses. This model has separated
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variability from uncertainty by identifying four key variables as uncertain, selecting values for

these variables according to specific distributions, and inserting these values into a model

simulation loop. In this manner, parameters that represent variability of the model are not

mixed with parameters that are uncertain. This separation has allowed us to analyze the

reduction in the overall uncertainty of the analysis that we will gain if the uncertainty of an

individual variable is reduced. Other microbial risk assessments have separated variability from

uncertainty; however, this risk assessment has investigated the gain in information that results

from reduction in uncertainty of individual variables. We discuss each of these points in turn.

This model analyzes risk within the four seasons for five primary harvesting regions (Northeast

Atlantic, Mid-Atlantic, Gulf of Mexico [divided into 2 regions], and Pacific Northwest), due to

differing harvest practices and climates. We could have subdivided the analysis further;

however, the limitations of acquiring the data for the next level down are such that we did our

analysis at the regional level. Analyzing the regions separately allows the assessors to look at

mitigations that may be tailored to specific regions and seasons. Those results may then be used

in a subsequent cost benefit analysis. Other microbial risk assessments have not performed

region/season analysis. This limits the range of possible mitigations that can be examined.

The model is amenable to further subdivision of locality and season because it is scalable. What

we mean by scalable is that the model simulates from harvest to consumption of oysters with a

specific level of V. parahaemolyticus. Given the existence of appropriate data, the model can

simulate this process from any specific harvest location at any specified time. Our analysis

(regional and seasonal) was done at the level at which we had the required data set of water and

air temperatures, harvest practices, V. parahaemolyticus prevalence, and shellfish landing

information and would give us a complete US risk assessment. We could have further refined

our assessment if we had complete data for the individual states, but time and resources were

limiting factors. If the data were available, the model could be applied to risk assessments at the

state level, shellfish harvesting area level, or, still finer, division of shellfish location and

season. New uncertainties will arise if the model is applied to harvesting areas for which the

required data set is incomplete. We were not able to explore the effect of incomplete (or

inaccurate) data on the results of the model at this time, but we may do analyses in the future.

We have separated variability from uncertainty in our analysis because it provides the best

characterization of the available information. We distinguish between model inputs that are less

well characterized because of our lack of knowledge (uncertainty) and model inputs that are

heterogeneous (variable). By describing some model inputs as heterogeneous we mean that we

characterize some of the model inputs as being naturally variable. For example, the water

temperatures of the different regions will vary in the model according to a normal distribution

with a given mean and standard deviation. At the same time, we have characterized the

conversion factor between V. parahaemolyticus growth in axenic culture and V.

parahaemolyticus growth in oysters as an uncertainty model input. We did this because of our

lack of knowledge of the true conversion factor. With more study we could reduce our

uncertainty. The result of our making the distinction between model inputs that are uncertain

and model inputs that are variable is that we may analyze our model and examine the effect on

output of reducing the uncertainty of each of the uncertainty variables separately. In this way

we provide our risk managers with insight on which uncertainty has the greatest effect on the
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final results. Risk managers may then prioritize which uncertainty to reduce first in an effort to

reduce the uncertainty inherent in the risk characterization of the risk assessment.

The model can be improved. At present, the model simulates risk for a set of uncertainty factors

with defined variability largely based on the relationship of V. parahaemolyticus levels to

temperature and a random selection (within defined limits) of percent pathogenic V.

parahaemolyticus. The model does not allow a quantitative prediction of the reduction in risk

resulting from implementation of the FDA/ISSC V. parahaemolyticus interim control plan

(adopted by the ISSC in July 1999), because we cannot model the effect on the risk from

embargoing subsequent harvesting after measuring an unsafe level of V. parahaemolyticus in a

shellfish harvesting area. For us to be able to model the plan, we have several modeling needs:

first, we need to scale the model to the shellfish harvesting level. To do this we need complete

data sets for the individual harvesting areas. Second, we will need sensitivity and specificity

data for the virulent V. parahaemolyticus gene probe methodology by the individual laboratories

doing the tests. Third, we will need to extend the model to account for rapidity by which

pathogenic V. parahaemolyticus levels change in specific areas. At present the model

predictions are primarily based on temperature plus a random factor for population variation.

The model might be improved by tailoring the rapidity of turnover of water in the shellfish

harvesting area based on levels of freshwater flows, tide changes, wind direction, and depth of

harvesting area. These are all areas of planned future study.
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VII. DISCUSSION

The objectives of the risk assessment were two-fold: (a) to create a mathematical model and

assess the current risk of becoming ill due to the consumption of pathogenic V.

parahaemolyticus in raw oysters; and (b) develop a comprehensive and current scientific

framework, which will assist the agency with the review of current programs relating to the

regulation of V. parahaemolyticus in raw molluscan shellfish to ensure that such programs

protect the public health. The risk assessment task force was also charged to evaluate: the

evidence for increased risks from specific newly emerging “outbreak strains”, the effectiveness

of potential strategies for limiting exposure of the public to raw molluscan shellfish, particularly

oysters containing pathogenic V. parahaemolyticus, the current criteria for opening and closing

harvest waters, and FDA’s current established guideline level of 10,000 V. parahaemolyticus/g

of food.

The risks for sporadic illnesses occurring due to V. parahaemolyticus in oysters are determined

by this risk assessment. Assessment of the risks associated with oyster-borne outbreaks caused

by pathogenic V. parahaemolyticus will not be feasible until a later date. The data needed to

model outbreak-associated risk are not as yet available. These data will be obtained from the

interim control strategy for preventing outbreaks caused by V. parahaemolyticus by monitoring

oyster meats for strains having the TDH gene, implemented in 1999 by the Interstate Shellfish

Sanitation Conference (ISSC). Monitoring for pathogenic strains and implementation of harvest

controls commenced in the spring of 2000 (some states began monitoring in 1999). To

reasonably assess the effectiveness of this interim control strategy, FDA needs the following

information: (a) which regions are monitoring, (b) the number of growing areas monitored in

each region, (c) the number of total samples collected and the number positive for V.

parahaemolyticus in each region, (d) the sensitivity of testing, (e) oyster landing data for each

site and region during periods of monitoring, and (f) case data on oyster-borne illnesses caused

by pathogenic V. parahaemolyticus.

The solicitation and assemblage of information and scientific data on V. parahaemolyticus from

many sources produced a thorough, up-to-date compilation. This information was used in the

construction of a mathematical model to produce results on the risk of illness incurred by eating

raw oysters containing pathogenic V. parahaemolyticus. Three basic factors were found to be

associated with this pathogen and consumer risk: the level of pathogenic V. parahaemolyticus in

seafood at harvest, effect of post harvest handling and processing, and the ability of the

organism to multiply to an infective dose. As a result, the risk assessment project was divided

into three separate modules, which corresponded to different stages leading potentially to

consumer exposure: the Harvest, the Post Harvest, and the Public Health Modules. The Harvest

Module estimated the prevalence of pathogenic V. parahaemolyticus at time of harvest. The

Post Harvest Module determined the role of post harvest processing and handling on the levels

of pathogenic V. parahaemolyticus at consumption. The Public Health Module estimated the

risk of illness caused by this organism. Because of harvesting and temperature differences, for

the purpose of the model, the United States harvest areas were divided into five regions, and

each region was divided into four seasons. Differences existing in oyster harvesting practices
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and climates in the United States were sufficiently significant to identify five separate

geographic regions (Northeast Atlantic, Mid-Atlantic, Pacific Northwest, Louisiana Gulf Coast,

and the remainder of the Gulf Coast) for each season, for consideration in modeling each of the

modules. Factors influencing the risk of illness posed by V. parahaemolyticus were identified

and incorporated in each module as appropriate. Integration of the various parameters

comprising these modules into a quantitative risk assessment model has provided a more

comprehensive understanding of the relative importance and interactions among these factors

influencing risk. This gain in understanding should serve to facilitate several processes,

including the formulation of effective guidance for the industry, regulators and consumers, the

evaluations of risk mitigation strategies, and the development of options and policies for

managing risk.

While providing a framework for understanding the relationship of risk to various parameters,

the development of the risk assessment model necessarily required certain assumptions to fill

the data gaps. The assumptions incorporated in the model were reviewed by NACMCF at a

public meeting in September1999. Based on the information currently available, for the Harvest

Module, it was assumed that the presence of the TDH gene be used as the basis for

pathogenicity. It is not currently known what average levels of TDH-positive strains actually

exist in shellfish, nationally or regionally. The estimates made in the V. parahaemolyticus risk

assessment, based on observed frequency of TDH-positive isolates, were the best possible with

the data currently available. However, since we do not know how this frequency may vary from

one year to the next, we assumed a 2-fold up or down triangle distribution. Also, within a given

year, we were unsure about the variance of percentage pathogenic in one composite of oysters

to the next. For example, outside of the Pacific Coast, percentage pathogenic V.

parahaemolyticus in a given year ranged from 0.1% to 0.3%; for the Pacific Northwest the

range used was 2% to 4%. Furthermore, these estimates are based on older data, and may not

be predictive of future years, given that frequency of percentage pathogenic V.

parahaemolyticus may be changing as new outbreak strains emerge or reemerge, such as the

emergence of O3:K6 or recurrence of known outbreak strains such as O4:K12.

For the Post Harvest Module, several assumptions were made based on the knowledge of

current post harvest practices and information available. The time oysters are harvested to the

time they are refrigerated was based on the current NSSP requirement(64) put into effect in

1997. The extent of growth that occurs during the period of time from harvest until the time

that oysters are first placed under refrigeration is determined by three factors: (a) the growth rate

of V. parahaemolyticus as a function of temperature; (b) the temperature of oyster meat after

harvest and (c) the length of time held unrefrigerated. The growth rate of pathogenic V.

parahaemolyticus in oysters was assumed to be one fourth that in broth culture at all

temperatures. This rate was based on the model of Miles et al. (95), and the corresponding

studies in oysters by Gooch et al. at 26o C (51). Also, since the V. parahaemolyticus organisms

do not change their growth environment after harvest (within the oyster meat), it was assumed

that lag time was negligible and was therefore omitted from the growth model. Regarding

growth rates, preliminary studies at GCSL, showed no significant difference between

pathogenic and non-pathogenic strains of V. parahaemolyticus. Since data on cooling rates of

commercial oyster shellstock could not be located, the time for oysters to cool after being

placed under refrigeration was assumed to be quite variable. This depended on efficiency of the
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cooler, quantity of oysters to be cooled and their arrangement in the cooler. A uniform

distribution between 1 and 10 hours was used to model this parameter based on preliminary

GCSL experiments for the time it took a single shell oyster at 30°C placed into a 3° C cooler to

reach that temperature, and the time it took for 24 oysters in an uninsulated plastic container at

26° C to reach 3° C.

For the sake of simplicity of the model, we assumed that consumption patterns were the same

for both the sensitive and otherwise healthy population, for all regions. It was assumed that all

virulent/pathogenic strains of V. parahaemolyticus are equally virulent with the same doseresponse as those strains fed to human volunteers in earlier studies. This assumption was based

on personal communication with Dr. Nishibuchi, Kyoto University (105), who stated that due to

lack of information, it is not known whether there are differences in virulence among different

strains.

Our model clearly illustrated that air and water temperatures were the driving factor for initial

pathogen loads as well as continued growth after harvesting, but there is some uncertainty due

to lack of data showing correlating V. parahaemolyticus levels. It is also noteworthy that in the

Pacific and Mid-Atlantic, the lower air temperatures reduce the importance of air temperature

and time unrefrigerated compared to the Gulf Coast.

The risk assessment model illustrated that the most significant factor influencing probability of

illness due to V. parahaemolyticus is the level of V. parahaemolyticus present in the oyster at

harvest. However, the model is based on a strong correlation between total and pathogenic V.

parahaemolyticus levels at time of harvest. We have also assumed that pathogenic strains of V.

parahaemolyticus grow at the same rate as non-pathogenic strains. Consequently, as the level

of total V. parahaemolyticus increases so does the number of pathogenic V. parahaemolyticus.

For the Gulf Coast, the second most influential factor for occurrence of illness is the duration

that oysters are left unrefrigerated after harvest. For the remaining regions modeled, i.e., MidAtlantic and Pacific Northwest, water temperature was the second most influential parameter.

For all regions, however, the amount of oysters consumed was the third most influential factor.

It is interesting to note that time the oysters were left unrefrigerated was more significant for the

Louisiana Gulf Coast than for the remaining Gulf Coast. It is known that many oyster

harvesting areas are further offshore in Louisiana than in the rest of the Gulf Coast, and

therefore it takes longer for the boats to return to the shore after harvest, leaving the oysters

unrefrigerated for a longer time period.

Modeling of the Post Harvest Module demonstrated that if oysters are not refrigerated rapidly

after harvest as recommended by NACMCF (102), V. parahaemolyticus rapidly multiply in

oysters resulting in much higher levels. The model’s simulation of mitigation strategies

indicated a significant reduction in the probability of illness when the oysters are cooled

immediately after harvest. Furthermore, V. parahaemolyticus densities were shown to decrease

slowly during refrigerated storage, as also stated by the MSI and PCSGA in response to the

Federal Register notice Docket No. 99N-1075 (43). Moreover, the use of mild heat treatment,

which causes at least a 4.5 log10 decrease in the number of viable V. parahaemolyticus in
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oysters, practically reduced to zero the probability of illness occurring. Freezing, which causes

a 1 to 2 log10 decrease substantially reduced the probability of illness.

Earlier human trials conducted in Japan showed an increase in the number of illnesses with

increasing levels of pathogenic V. parahaemolyticus. Different dose-response models were

compared for the purpose of extrapolating risk of illness estimated on the basis of human

feeding trials at high levels of exposure to the lower levels of exposure associated with

consumption of raw oysters. However, consideration of CDC estimates of annual illness

suggested that the dose-response under conditions of population exposure was different than

that observed in human volunteer studies. In other words, direct extrapolation of the doseresponse under conditions of exposure in the feeding trials is not supported by the

epidemiological data. The human feeding trials were conducted under conditions of concurrent

antacid administration. Due to possible food matrix effects of the oyster, dose-response was

shifted by 1 log10 from that based on published clinical trials. Preliminary data have shown that

this shift is "supported" by consideration of the CDC numbers of V. parahaemolyticus infection.

Distributions of ingested dose were developed by considering the probabilistic variation of

number and meat weight of oysters in a serving in addition to the expected variation of the

density of pathogenic V. parahaemolyticus determined in the Harvest and Post Harvest

Modules.

The outputs from this project provide estimates of risk for illness among consumers of raw

oysters (average nationwide yearly incidence of 4,750 cases per year, with a range from 1,000

to 16,000 cases - for the Gulf Coast, 25 (winter), 1,200 (spring), 3,000 (summer), and 400 (fall);

for the Pacific Northwest, 15 (spring) and 50 (summer); for the Mid-Atlantic, 10 (spring) and 12

(summer); and for the Northeast Atlantic, 12 (spring), 30 (summer) and 7 (fall)). Risks increase

with increasing levels of total V. parahaemolyticus and therefore pathogenic strains of V.

parahaemolyticus.

The model made it possible to develop a mathematical means of relating potential

microbiological criteria with both the predicted percentage of illness prevented and the

predicted percentage of the oyster landings that would no longer be available to consumers if

the criterion could be implemented with 100% efficiency. Retail surveys of oysters, clinical

studies and outbreak investigations, have shown that the guidance level of 10,000 viable V.

parahaemolyticus cells/gram of oyster meat may not be relevant to safety. Simulations on the

rate of illness caused by oyster-servings where the levels of V. parahaemolyticus at harvest are

at or above 10,000 cells/g suggest that approximately 15% of the illnesses are associated with

the consumption of oysters containing greater than 10,000 V. parahaemolyticus/g at time of

harvest. This is a consequence of the fact that higher levels of pathogenic V. parahaemolyticus

are more likely to occur with higher levels of total V. parahaemolyticus. Nevertheless, even at

these high levels, the risk of illness per serving is still comparatively low. Comparing the

number of servings that cause illness to those that don't, the simulations demonstrate that on

average 0.6% of the servings result in illness when V. parahaemolyticus levels are at 10,000

cells/g or above.

The risk assessment team addressed the questions that it was charged with as described below.
VII. DISCUSSION

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ÿ What is the frequency and extent of pathogenic strains in shellfish waters and shellfish?

• There is a need for more information on virulence factor determination, such as invasion

of the enterocytes (3), and production of an enterotoxin (61), and particularly, urease

production (77, 79). Since the data on levels of pathogenicity pose a large uncertainty,

the model was run with a range of estimates for TDH, from 0.0032%, to 3.2%. This

produced a range of possible illness rates, with the most likely being identified (along

with the reasons why it seems the most likely) as the most probable level of risk.

• The epidemiology and pathogenicity of V. parahaemolyticus will be better understood

once the ecological nuances of the microbe-host dynamics in the estuarine ecosystem are

elucidated.

ÿ What parameters can predict presence?

• Our model clearly illustrated that air and water temperatures were the driving factor for

initial pathogen loads as well as continued growth after harvesting, although some

uncertainty exists due to lack of direct measurement of the relationship in regard to

pathogenic V. parahaemolyticus levels per se.

• The literature has shown that other factors not incorporated into the model may also

predict presence of pathogenic V. parahaemolyticus. These include:

♦ Salinity - The Texas outbreak showed a significant increase in the salinity levels and

temperature in the spring (May-June) of 1998 compared with the previous five years,

however these levels are very similar to normal summer temperature and salinity

levels. It is also possible that the sudden increase in temperature/salinity causes the

V. parahaemolyticus, both the pathogenic and non-pathogenic strains to grow

quickly or multiply. However, our model suggested that salinity was not, in fact, an

important variable.

♦ Ballast waters - Although there was insufficient data to quantitatively model this

parameter, ballast waters have been associated with Vibrio illnesses. (93).

♦ Other factors, which were also not quantitatively modeled, such as immune status of

the oyster, have also been shown from the literature to play an important role in the

prevalence of pathogenic V. parahaemolyticus. Oyster data from the DePaola et al.

study (38) on the incidence of V. parahaemolyticus in U.S. coastal waters and

oysters were probably representative of different physiological states of the oysters.

Once sufficient data have been accumulated to actually investigate the V.

parahaemolyticus load and health of the oysters, the information can be incorporated

into the model. There is a need to correlate the number of V. parahaemolyticus with

the percentage oysters diseased.

ÿ How do levels at consumption compare to initial levels?

• Preliminary analysis of the ISSC/FDA retail study showed higher V. parahaemolyticus

densities at retail than at harvest for the Gulf Coast. Model predictions suggest that the

difference between densities at harvest versus those at time of consumption is largely

attributable to the extent of growth that occurs after oyster harvest is landed and has not

yet been cooled to no-growth temperatures. The extent of the difference varies by

region and is largest during the summer ranging from 0.50 for the Pacific Northwest to

1.25 log10 for the total Gulf Coast. During the spring the difference between densities at
VII. DISCUSSION

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harvest versus time of consumption is approximately 0.75 log10 for the Gulf Coast and

somewhat less for other regions of the country. The simulation results are consistent

with the V. parahaemolyticus densities observed at retail in the ISSC/FDA study.

ÿ What is the role of post harvest handling?

• The model demonstrated that if oysters are not refrigerated after harvest, V.

parahaemolyticus rapidly multiply in oysters resulting in much higher levels. The model

revealed a significant reduction in the probability of illness when the oysters are cooled

immediately after harvest. Furthermore, V. parahaemolyticus densities are shown to

decrease slowly during refrigerated storage.

ÿ What intervention strategies can be used?

• Our simulation of post harvest mitigation strategies has shown that mild heat treatment

which causes at least a 4.5 log decrease in the number of V. parahaemolyticus essentially

eliminates any probability of illness.

• Freezing combined with frozen storage, which causes a 1 to 3 log decrease substantially

reduces the probability of illness.

ÿ What is known about dose-response?

• Early Japanese feeding studies described in previous sections, have shown an increase in

number of illnesses with increasing levels of pathogenic V. parahaemolyticus.

However, the extent to which the estimate of the ID50 under conditions of human feeding

trials underestimates the "true" population ID50 is unknown. In consideration of CDC

estimates of annual illness rates, model predictions based on projected levels of

pathogenic V. parahaemolyticus suggest that the dose-response for illness under

conditions of normal population exposure may be significantly different from the

conditions of the feeding trials. Possible reasons for the difference include food matrix

or immunological effects of preexposure to the organism including antibodies/vaccines

to the organism (88).

ÿ How does dose-response vary for different strains of V. parahaemolyticus?

• Based on insufficient epidemiological data and personal communication with Prof.

Mitsuaki Nishibuchi who suggested that more research is needed to determine whether

strains differ in virulence (105), we have assumed equal virulence for all virulent strains.

The assessment of dose-response according to virulence was developed based on the

organism’s ability to produce TDH. This may be modified as new data become

available that identify new virulence determinants. As mentioned previously, other

strain characteristics, such as invasion of the enterocytes (3), and production of an

enterotoxin (61) not normally investigated in the environmental or in clinical isolates,

may also be important in characterizing pathogenicity. In particular, recent data from

British Columbia (77) may suggest an association of urease positive strains with clinical

isolates, which may or may not be TDH positive.

ÿ How does dose-response vary among humans with different susceptibilities?

• Epidemiological data indicate that the whole population is susceptible to infection.
VII. DISCUSSION

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• Differences in dose-response among humans with different susceptibilities, are not as yet

known. However, given infection, regardless of dose, there is a greater probability of

infection leading to more severe illness, such as septicemia and death, among the

subpopulation with a concurrent underlying medical condition. As mentioned in the Risk

Characterization section, using case series data provided by the CDC, diarrhea in an infected

person has a 12% chance of going on to septicemia if that person belongs to the sensitive

subpopulation, and V. parahaemolyticus has been culture-confirmed.

ÿ Is current knowledge adequate in assessing the risk of consuming raw oysters containing

pathogenic V. parahaemolyticus?

• Our gain in understanding of the relative importance and interactions among the factors

influencing risk should serve to facilitate several processes, including the formulation of

effective guidance for the industry, regulators and consumers, the evaluations of risk

mitigation strategies, and the development of options and policies for managing risk.

• However, the risk assessment also identified data gaps, which will need to be addressed

by research.

ÿ Where should future research be directed to reduce uncertainty in risk estimate?

• Future research should be directed at the data gaps as listed below

Data Gaps and Future Research Needs

Deficiencies of the current research with respect to risk assessment were identified in order to

suggest future research or further data gathering to reduce uncertainties.

ÿ Incidence/frequency of pathogenic V. parahaemolyticus in water and shellfish.

• Factors that affect incidence of pathogenic V. parahaemolyticus in the environment.

• Role of oyster physiology and immune status in levels of V. parahaemolyticus. There is

a need to correlate the number of V. parahaemolyticus with the percentage oysters

diseased.

• More research on the potential virulence factors of pathogenic strains other than TDH,

e.g. urease, enterotoxins. V. parahaemolyticus strains that do not produce TDH, TRH, or

urease have recently been found to induce fluid accumulation in suckling mice and

diarrhea in a ferret model after oral inoculation in a dose-dependent manner (84).

Correlation between clinical and environmental incidence of these strains is yet to be

determined.

ÿ Growth rate of V. parahaemolyticus within oysters at temperatures other than 26° C;

including the issue of potential differences in the growth rate of pathogenic strains versus

total V. parahaemolyticus populations.

ÿ Rates of hydrographic flushing (water turnover) in shellfish harvest areas based on levels of

freshwater flows, tidal changes, winds, depth of harvesting area and how these factors may

influence pathogenic V. parahaemolyticus levels.
VII. DISCUSSION

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ÿ Dose-response data.

• FDA is currently funding a cooperative agreement with the University of Maryland to

acquire a dose-response curve for V. parahaemolyticus in humans after extrapolation

from animal studies. Volunteers will be fed raw oysters containing V. cholerae non-O1

at varying doses. Animals will be fed with V. cholerae non-O1 and V. parahaemolyticus

at varying doses. After the animal doses and strains have been compared, the V.

parahaemolyticus dose-response observed in animals will be extrapolated to humans,

based on the data from the clinical and animal studies with V. cholerae non-O1. Better

dose-response data will be available within the next two years after the completion of

this study.

• More intensive investigations of shellfish foodborne disease outbreaks in such a way as

to examine the relationships between the dose of contaminated food items ingested and

the severity of the resulting illness controlling for host factors.

ÿ More data from State surveillance systems.

ÿ Consumer handling of oysters.

ÿ Improved global public health surveillance of V. parahaemolyticus to identify new epidemic

strains as they emerge.

• FDA is already currently involved in several collaborative efforts with the ISSC and the

states, to protect the public from illness caused by V. parahaemolyticus and other seafood

pathogens. Some of these efforts have provided invaluable information for the risk

assessment. Data acquired in the future from the other efforts (listed below) will be used to

validate and update our model.

•

ÿ The V. parahaemolyticus interim control plan for closing and reopening harvest waters was

adopted by the ISSC in July, 1999, for areas confirmed as an original source of oysters

harboring pathogenic (TDH+) V. parahaemolyticus, that have been associated with two or

more confirmed illnesses within the last three years. The outcome of modeling this plan

will also enable the evaluation of the criteria for closing and reopening harvest waters.

When the requisite data become available, it is anticipated that the reduction in risk afforded

by the ISSC prevention strategy as well as the criteria for opening and closing harvest

waters can be modeled.

ÿ Oyster harvest monitoring survey – FDA in conjunction with the ISSC and the states is

conducting a study to determine the levels of V. parahaemolyticus in oysters in the growing

areas before they are harvested. This study was initiated in 1998, and some of the data

obtained thus far, was incorporated into the risk assessment when determining levels of total

and pathogenic V. parahaemolyticus.
VII. DISCUSSION

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ÿ The time to refrigeration data obtained from states that had had V. vulnificus illnesses,

proved to be invaluable in providing the data for the time to temperature simulations in the

Post Harvest Module.

In conclusion, this risk assessment significantly advances our ability to describe our current

state of knowledge about this important foodborne pathogen, while simultaneously providing a

framework for integrating and evaluating the impact of new scientific knowledge on enhancing

public health. 

The results of this draft risk assessment on V. parahaemolyticus are influenced by the

assumptions and data sets that were used to develop the exposure assessment and hazard

characterization. These results, particularly the predicted estimates of risk for illness among

consumers of raw oysters, and the most significant parameters, which influence the incidence of

illness, could change as a result of future data obtained from the Interim Control Plan and the

FDA actively seeking new information, scientific opinions, or data during the public comment

period. It is anticipated that periodic updates to the risk model will continue to reduce the

degree of uncertainty associated with risk estimates, and that this will assist in making the best

possible decisions, policies, and measures for reducing the risk posed by V. parahaemolyticus in

raw molluscan shellfish.
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IX. APPENDICES

Appendix I

Chronology of Technical and Scientific Reviews of the FDA Vibrio parahaemolyticus

Risk Assessment Document 

We solicited the advice and opinions of State shellfish and scientific experts, and the public

throughout the conduct of this Vibrio parahaemolyticus risk assessment. A summary of the

dates, type of review activity, and participants is provided on the next page.
IX. APPENDICES

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Chronology of Technical and Scientific Reviews of the FDA Vibrio parahaemolyticus Risk

Assessment

Date Activity Participants

January 1999 Risk Assessment Team assembled FDA

May 7, 1999

Federal Register Notice; request for

comments and for scientific data and

information

Public

May 27, 1999 Public meeting (Chicago, IL)

NACMCF; Public; VPRA

team

members

August 13, 1999 Federal Register Notice of public meeting Public

September 24, 1999

Public meeting; request for comments on

the risk assessment approach and

assumptions (Washington, DC)

NACMCF; Public; VPRA

team

members

December 1999 Request for scientific review of draft risk

assessment document RAC members

December 1999 Technical discussion of the draft risk

assessment document

RAC annual meeting

(closed)

December 1999 Intensive review of model Dr. David Gaylor,

FDA/NCTR

March 31, 2000 Internal scientific review of draft document Selected FDA risk

managers

May 29, 2000 Technical review of document Selected government

experts and SGE’s

May 29, 2000 Review of model and mathematics Selected government

experts and SGE’s

July 28, 2000 Internal scientific review of draft document Selected FDA risk

managers

August 4, 2000

Presentation on update of V.

parahaemolyticus risk assessment at the

IAFP meeting, Atlanta, GA

IAFP attendees

NACMCF - the National Advisory Committee on Microbiological Criteria for Foods.

RAC - the U.S. government Interagency Risk Assessment Consortium

SGE - Special Government Employees

IAFP - International Association for Food Protection
IX. APPENDICES

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Appendix II.

Model for the FDA Vibrio parahaemolyticus Risk Assessment

Electronic copy available: http://www.foodsafety.gov/~dms/fs-toc.html