Vibrio in seafood - FAO.org

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Clearly, in order to predict the distribution of V. parahaemolyticus numbers at harvest, based onthe 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-shorebuoy data available from the National Buoy Data Center in the United States, regional and seasonaldistributions of water temperature were available for the FDA-VPRA (Anonymous, 2001). 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 for the United States.Two considerations suggest that neglecting the effect of salinity does not adversely affect thepredictive value of a model based on temperature alone. First, as shown in Figure 2.6, predicted meanV. parahaemolyticus numbers vary by less than 10% from the optimal (maximum) number as salinityvaries from 15-30 ppt. Secondly, measurements of oyster liquor salinity at the retail level, which arestrongly correlated with the salinity of harvest waters (FDA, 2000), suggest that oysters may be harvestedfrom the more saline areas of the estuaries year round. In one survey (n=249) conducted year round withsamples obtained from all regions of the United States the mean oyster liquor salinity was found to be 24ppt (SD = 6.5 ppt) (ISSC & FDA, 1997). These two considerations suggest that the effect of variation ofsalinity on predicted distributions of V. parahaemolyticus numbers is minor. Variations in salinitybetween 15 and 30 ppt would increase the variance of the predicted distribution by only a small amount.Ignoring the effect of variations in salinity in the simulation can be accomplished in either of twoways. Either salinity can be fixed to a mean value (i.e. 22 ppt) in the regression relationship derived aboveor the prediction of V. parahaemolyticus numbers can be based on a regression analysis of the data fromDePaola et al. (1990) with water temperature as the only effect in the model.With water temperature as the only effect the regression equation is:log( Vp / g)= α + β ∗TEMP+ εwhere TEMP denotes the temperature in °C, α and β are regression parameters for temperature effect onmean log 10 numbers, and ε is a random normal deviate with zero mean and variance σ 2 corresponding tothe combined effects of population and method error variation.Parameter estimates obtained based on the Tobit estimation method areα = −1.03β = 0.12σ2 =1.1Based on the data of DePaola et al. (1990), the estimate of the variance about the mean (σ 2 ) is aninflated estimate of V. parahaemolyticus population variation due to method error. An estimate ofpopulation variation about the mean is obtained by subtracting an estimate of the method error. Because aTobit estimation method is used, standard R 2 statistics cannot be used to characterize the correlation of themodel to the data, and a pseudo-R 2 analysis was used. The pseudo-R 2 maximum likelihood R 2 (Maddala's)value of 0.47 was obtained (Long and Freese, 2000). In the study of DePaola et al. (1990) enumeration ofV. parahaemolyticus, following the HGMP procedure, was based on testing of five suspect colonies andconsequently was not as precise as possible and overall method error associated with estimatingV. parahaemolyticus numbers may have been more comparable with that of a three tube MPN procedure.An estimate of the method error variance of the three tube MPN procedure is 0.35 (DePaola et al., 2000)and this value was considered a reasonable estimate of the method error for the methodology used in thestudy of DePaola et al. (1990).20
The predicted mean log V. parahaemolyticus level versus temperature for the temperature-onlyregression is shown in Figure 2.7. Clearly, this relationship is comparable with that which would beobtained by fixing the salinity to a near optimal value (22 ppt) in the prediction equation based on bothwater temperature and salinity. The temperature-only regression was used to model the relationshipbetween temperature and density of total V. parahaemolyticus at time of harvest.4.53.52.51.50.50 5 10 15 20 25 30 35Water temperature (º C)limit ofdetectionFigure 2.7: Observed log 10 V. parahaemolyticus (Vp) numbers in oysters versus water temperature atdifferent salinities (20 ppt (○)) in comparison to predicted log 10numbers (solid line) and 95% confidence limits (dashed lines) based on temperature only regressionmodel.2.2.5.3.2 Water Temperature DistributionsIn the United States FDA-VPRA (Anonymous, 2001), regional and seasonal distributions of watertemperatures were developed based on accumulated records from coastal water buoys (National Buoy DataCenter data). Seasons were defined by calendar month: winter (January-March), spring (April-June),summer (July-September) and fall (October-December). For each region and season a shallow water buoywas selected as representing the water temperature distribution for oyster harvest areas within thatregion/season combination. The available database for most buoys has hourly water temperatures from1984 up to the present time, with occasional data gaps due to instrumentation malfunction. The correlationbetween water temperature and the ambient air temperature that oysters are subject to after they areharvested was accounted for by selecting buoys for which air temperature records were also available.Because oyster harvesting in the United States outside of the Pacific Coast region commencesearly in the morning and ends mid or late afternoon, the daily water temperature recorded at noon wasconsidered to represent an average daily temperature. The distribution of these "average" temperatureswithin a given region and season varies from year-to-year with wider variations occurring during thetransitional seasons of spring and fall.Within a given year, the distribution of the noon water temperature was found to be unimodalwithin a given range. This empirical distribution is adequately approximated as a normal distributionprovided that no weight is given to implausible values outside the historical range of values that may be21
Page 1 and 2: Food and Agriculture Organization o
Page 3: 4.2.5 Simulation Results...........
Page 6 and 7: United States prior to 1997 illness
Page 8 and 9: 2 RISK ASSESSMENT OF VIBRIO PARAHAE
Page 10 and 11: The approach being taken is to use
Page 12 and 13: 2.2.2 Growth and survival character
Page 14 and 15: given average temperature and predi
Page 16 and 17: strains have been identified as a p
Page 18 and 19: 2001) and so this assumption may ha
Page 20 and 21: States. The study was undertaken on
Page 22 and 23: The estimated relationships between
Page 26 and 27: expected. Differences in these dist
Page 28 and 29: measurements were recorded for any
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
Page 48 and 49: Bean, N. H., Maloney, E. K., Potter
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
Page 62 and 63: 3.2.4 Preparation and consumption m
Page 64 and 65: • Frequency of consumption of raw
Page 66 and 67: Sarkar, B. L., G. B. Nair, et al..
Page 68 and 69: foodborne case has been associated
Page 70 and 71: (Cook and Ruple, 1992). However, th
Page 72 and 73: 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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