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2001) and so this assumption may have to be revised when applying the model to other countries orregions.For the Post Harvest Module, several assumptions were made based on the knowledge of thecurrent post harvest practices and information available in the United States but again these assumptionsmay have to be revised when information on the practices in other countries becomes available. The timeoysters are harvested to the time they are refrigerated was based on the current NSSP requirement (ISSC &FDA, 1997) put into effect in 1997 in the United States. The extent of growth that occurs during theperiod from harvest until oysters are first placed under refrigeration is determined by three factors:• Growth rate of V. parahaemolyticus as a function of temperature• Temperature of oyster meat after harvest• Length of time held unrefrigerated.The growth rate of pathogenic V. parahaemolyticus in oysters was assumed to be one fourth thatin broth culture at all temperatures (see section 2.2.2.1). Also, since the V. parahaemolyticus organisms donot change their growth environment after harvest (within the oyster meat), it was assumed that lag timewas negligible and was therefore omitted from the growth model. Regarding growth rates, preliminarystudies at the Gulf Coast Seafood Laboratory (GCSL) in the United States showed no significant differencebetween pathogenic and non-pathogenic strains of V. parahaemolyticus.Since data on cooling rates of commercial oyster shellstock has not been located to date, the timefor oysters to cool after being placed under refrigeration was assumed to be quite variable. Cooling ratesare dependent on the efficiency of the cooler, the quantity of oysters to be cooled and their arrangement inthe cooler. A uniform distribution between 1 and 10 hours was used to model this parameter. This wasbased on preliminary experiments carried out at GCSL in the United States for the time it took a singleshell oyster at 30°C placed into a 3°C cooler to reach that temperature, and the time it took for 24 oystersin an uninsulated plastic container at 26° C to reach 3° C.For the sake of simplicity of the model, it was assumed that consumption patterns were the samefor both the sensitive and otherwise healthy population, for all regions. It was assumed that allvirulent/pathogenic strains of V. parahaemolyticus are equally virulent with the same dose-response asthose strains fed to human volunteers in earlier studies. This assumption was based on personalcommunication with Dr. Nishibuchi, Kyoto University, Japan who stated that due to lack of information, itis not known whether there are differences in virulence among different strains.2.2.5.3 Harvest moduleAlthough a number of factors have been identified as potentially affecting the numbers ofpathogenic V. parahaemolyticus in oysters at time of harvest, there are not sufficient quantitative dataavailable to incorporate all of these factors into a predictive model. Incorporation of an environmentalfactor into the simulation as a predictor of V. parahaemolyticus numbers at harvest requires both therelationship between V. parahaemolyticus numbers to the parameter of interest and the regional andtemporal variation of the parameter within the environment to be identified. Moreover, due to therelatively low prevalence of pathogenic V. parahaemolyticus and limitations of current methods ofdetection, the distribution of pathogenic V. parahaemolyticus is not well understood. A critical issue in thedevelopment of the Harvest Module simulation is the use of the estimated distribution of totalV. parahaemolyticus numbers to bridge this data gap and derive an estimate of the distribution ofpathogenic V. parahaemolyticus numbers in oysters at harvest. Figure 2.3 illustrates the parametersconsidered in modelling the Harvest Module.The main factors that have been identified as potentially affecting the numbers of pathogenicV. parahaemolyticus in oysters at the time of harvest are water temperature and salinity and this issue isaddressed in more detail below. The preliminary modelling demonstrated that the parameter, water14
salinity is not as strong a determinant of V. parahaemolyticus numbers as water temperature, and thereforeis represented as a dotted bubble in Figure 2.3.Regional, seasonal & yearly variationWater temperatureWater salinitytotal Vp/gpathogenic Vp/gFigure 2.3: Schematic depiction of the Harvest Module of the V. parahaemolyticus (Vp) risk assessmentmodel.2.2.5.3.1 Effect of water temperature and salinity on prevalenceA number of studies have been carried out on the prevalence of V. parahaemolyticus, but many ofthese report only presence or absence V. parahaemolyticus, which are of limited value for quantitative riskassessment (Hanrihan et al., 1995, Kelly and Stroh, 1988a; Kiiyukia et al., 1989). Some studies which didmeasure V. parahaemolyticus numbers in oysters were also limited because samples were either obtainedfrom a single estuary (Chan et al., 1989; Hanrihan et al., 1995; Kaysner et al., 1990b; Kelly and Stroh,1988a; Kelly & Stroh 1988b; Kiiyukia et al., 1989; Tepedino, 1982), were not seasonal (Chan et al., 1989;Kaysner et al., 1990b; Tepedino, 1982), or did not report salinity and temperature (Chan et al., 1989;Tepedino, 1982). A number of there are summarised in Table 2.3.The limitations to the methodology currently used in the enumeration of V. parahaemolyticus havebeen acknowledged. In a study that evaluated four methods for enumerating V. parahaemolyticus innatural seawater and oysters, it was found that there was considerable variability between methods forV. parahaemolyticus recoveries, with highest recoveries being obtained using filtration through ahydrophobic grid membrane (DePaola et al., 1988). In a subsequent study DePaola et al. (1990) used thehydrophobic grid membrane filtration (HGMF) procedure developed by Watkins et al. (1976) and laterrevised by Entis & Boleszczuk (1983). When all suspect colonies are tested for confirmation, the precisionof the HGMF method has been shown to be greater than the three tube most probable number (MPN)procedure (Entis & Boleszczuk, 1983; Watkins et al., 1976).The most informative study currently available was found to be that on the enumeration ofV. parahaemolyticus (hydrophobic grid method) in seawater and oyster samples collected from May 1984through April 1985 from shellfish growing areas in the Pacific, Gulf and Atlantic Coasts of the United15
Page 1 and 2: Food and Agriculture Organization o
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Page 8 and 9: 2 RISK ASSESSMENT OF VIBRIO PARAHAE
Page 10 and 11: The approach being taken is to use
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Page 14 and 15: given average temperature and predi
Page 16 and 17: strains have been identified as a p
Page 20 and 21: States. The study was undertaken on
Page 22 and 23: The estimated relationships between
Page 24 and 25: Clearly, in order to predict the di
Page 26 and 27: expected. Differences in these dist
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Page 30 and 31: egions of the country the average p
Page 32 and 33: etained. A corresponding air temper
Page 34 and 35: Frequency of duration of oysterharv
Page 36 and 37: 2.2.5.4.5 Die-off of V. parahaemoly
Page 38 and 39: V. parahaemolyticus cause illness -
Page 40 and 41: and four of those patients died. Pa
Page 42 and 43: Vomiting 52 17-79Headache 42 13-56F
Page 44 and 45: 2.3.3.2 Dose-response model2.3.3.2.
Page 46 and 47: While these assumptions are not rep
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Page 50 and 51: Gjerde, E.P. and Bøe, B. 1981. Isp
Page 52 and 53: Levine, W. C., Griffin, P. M., and
Page 54 and 55: Abstract of the National Shellfishe
Page 56 and 57: 3.1.2 Prevalence in foodAlthough li
Page 58 and 59: The concentration of V. parahaemoly
Page 60 and 61: Figure 3.3. Schematic representatio
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Page 64 and 65: • Frequency of consumption of raw
Page 66 and 67: Sarkar, B. L., G. B. Nair, et al..
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foodborne case has been associated
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(Cook and Ruple, 1992). However, th
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4.2.4.1 HarvestLike V. parahaemolyt
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Although not incorporated in the pr
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increase in log of Vv density32.521
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Figure 4.6 shows typical distributi
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1.5Probability Density0.7501 2 3 4
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Table 4.6. Prevalence rates of V. v
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4.3.3 Dose-response relationship4.3
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Dec 15 0.76 0.61 1,841,000 1,025,00
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• Does virulence vary by season,
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ICMSF. 1996. Microorganisms in Food
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Appendix.Analytica 2.0.5 Model Simu
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The chance nodes representing varia
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5.2.2.2 Death or inactivationV. cho
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Table 5.3. Instances of fish implic
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Shrimp harvested fromcoastal areas
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O1 through poor quality water, and
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5.3.1.2 Characteristics of the host
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5.3.3.1.4 Probability of death give
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Resampled Beta-Poisson dose-respons
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De Paola, A. 1981. Vibrio cholerae
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Reilly, L.A. and Hackney, C.R. 1985
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