A quantitative risk assessment model for Vibrio parahaemolyticus in raw oysters in Sao Paulo State, Brazil

International Journal of Food Microbiology 180 (2014) 69–77

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International Journal of Food Microbiology journal homepage: www.elsevier.com/locate/ijfoodmicro

A quantitative risk assessment model for Vibrio parahaemolyticus in raw oysters in Sao Paulo State, Brazil Paulo de S. Costa Sobrinho 1, Maria T. Destro, Bernadette D.G.M. Franco, Mariza Landgraf ⁎ Department of Food and Experimental Nutrition, Faculty of Pharmaceutical Sciences, University of Sao Paulo, Brazil University of São Paulo – NAPAN – Food and Nutrition Research Center, Brazil

a r t i c l e

i n f o

Article history: Received 19 March 2013 Received in revised form 13 January 2014 Accepted 4 April 2014 Available online 13 April 2014 Keywords: Probabilistic risk assessment Vibrio parahaemolyticus Shellfish Crassostrea brasiliana Food safety

a b s t r a c t A risk assessment of Vibrio parahaemolyticus associated with raw oysters produced and consumed in São Paulo State was developed. The model was built according to the United States Food and Drug Administration framework for risk assessment. The outcome of the exposure assessment estimated the prevalence and density of pathogenic V. parahaemolyticus in raw oysters from harvest to consumption. The result of the exposure step was combined with a Beta–Poisson dose–response model to estimate the probability of illness. The model predicted that the average risks per serving of raw oysters were 4.7 × 10−4, 6.0 × 10−4, 4.7 × 10−4 and 3.1 × 10−4 for spring, summer, fall and winter, respectively. Sensitivity analyses indicated that the most influential variables on the risk of illness were the total density of V. parahaemolyticus at harvest, transport temperature, relative prevalence of pathogenic strains and storage time at retail. Only storage time under refrigeration at retail showed negative correlation with the risk of illness. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Vibrio parahaemolyticus is a Gram-negative, halophilic marine bacterium that occurs naturally in estuaries and commonly found in many types of seafood (Drake et al., 2007). V. parahaemolyticus has been recognized as a cause of gastroenteritis linked to the consumption of seafood, particularly oysters consumed raw or inadequately cooked or contaminated after cooking (Anonymous, 2009; Cabello et al., 2007; Drake et al., 2007; Harth et al., 2009; Lozano-Leon et al., 2003; Martinez-Urtaza et al., 2008; Ottaviani et al., 2008; Su et al., 2005; Yamamoto et al., 2008). Despite the large length of the Brazilian coast, little is known on the occurrence of infections caused by V. parahaemolyticus, certainly because notification of this type of infection is not mandatory (Leal et al., 2008). In recent years, quantitative microbiological risk assessment has emerged as a tool for assisting development of management strategies to improve food safety and safeguard public health, and risk assessments became a priority for the Food and Agricultural Organization and World Health Organization (Codex, 1999). Quantitative risk assessments of V. parahaemolyticus in seafood, including oysters, were conducted by FAO/WHO (FAO/WHO, 2002, 2011) based on a framework developed ⁎ Corresponding author at: Departamento de Alimentos e Nutrição Experimental, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, Av. Prof. Lineu Prestes, 580, B.14, 05508-900 Sao Paulo, SP, Brazil. Tel.: +55 11 30912199; fax: +55 11 38154410. E-mail addresses: [email protected] (P.S. Costa Sobrinho), [email protected] (M. Landgraf). 1 Present address: Department of Nutrition, Federal University of Jequitinhonha and Mucuri Valleys, Minas Gerais, Brazil.

http://dx.doi.org/10.1016/j.ijfoodmicro.2014.04.008 0168-1605/© 2014 Elsevier B.V. All rights reserved.

by the U.S. Food and Drug Administration for V. parahaemolyticus (U.S. FDA, 2005). Several oyster-producing areas are found along the Brazilian coast. The average annual oyster production in Brazil is approximately 142.4 tons. In the southeast coast of Sao Paulo State, oyster production is concentrated in the Cananeia lagoon estuarine region (25°00′53″S and 47°55′36″W) and between 2005 and 2010, this region was responsible for 98.5% of the production in the state of Sao Paulo (Anonymous, 2011). The objective of this study was to conduct a quantitative risk assessment for V. parahaemolyticus in raw oysters produced in Sao Paulo State, Brazil, based on the model developed by the U.S. Food and Drug Administration (U.S. FDA, 2005) and used by FAO/WHO to estimate the risk of illness associated to the consumption of raw oysters in other countries (FAO/WHO, 2011). 2. Material and methods 2.1. Overview of the risk assessment model The risk assessment model was composed of three modules, comprising harvest, post-harvest and consumption steps (Fig. 1). In the harvest module, factors influencing abundance of V. parahaemolyticus in oysters at the time of harvest were considered. The distribution of abundance was estimated by using a relation between prevalence of V. parahaemolyticus in oysters, seawater temperature, distribution of the water temperature in each season and relative prevalence of pathogenic V. parahaemolyticus in oysters. In the post-harvest module, factors

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Fig. 1. Conceptual model for the assessment of exposure of Vibrio parahaemolyticus in oysters, from farm to fork (Vp = V. parahaemolyticus).

associated with transportation, manipulation and depuration were considered. In the consumption module, the factors that were considered were amount of oysters consumed per serving, average weight of each oyster consumed and predicted distribution of pathogenic V. parahaemolyticus (Table 1). 2.2. Exposure assessment 2.2.1. Harvest module Seawater and air temperature data in Cananeia, SP, Brazil, were provided by the Department of Physical Oceanography, University of Sao Paulo. The data used for estimating the relation between the population of pathogenic V. parahaemolyticus in oysters and seawater temperature and salinity, and between pathogenic and non-pathogenic V. parahaemolyticus in oysters were obtained from Sobrinho et al. (2010). It was assumed that the abundance of V. parahaemolyticus at the time of harvest follows a lognormal distribution (Vose, 2000), with mean log levels proportional to the daily mean temperature and water salinity. It was also assumed that the variability of water temperature for all harvest sites throughout the year is representative of the mean temperature obtained for the city of Cananeia, Sao Paulo, and that the intertidal exposure of oysters to the air during low tide does not promote significant changes in the populations of V. parahaemolyticus. In addition, it was assumed that the variation in the relative prevalence of pathogenic strains of V. parahaemolyticus in relation to the prevalence of V. parahaemolyticus in oysters follows a Beta distribution, and that the relation between prevalence of pathogenic and nonpathogenic V. parahaemolyticus in the oysters does not vary throughout the year and is the same at all sites of harvest (Table 1). 2.2.1.1. Distribution of water temperature. Table 2 shows the mean daily seawater temperature in years 2002, 2003 and 2004, according to the

season in Brazil. Seasons were defined by calendar month as follows: winter (July–September), spring (October–December), summer (January–March) and autumn (April–June). The daily water temperature recorded at noon was chosen to represent the average daily temperature, because oyster harvesting in Cananeia region begins early in the morning and ends early afternoon. Temperature data of seawater in each season from 2001 to 2004 were fitted to a normal distribution. The adequacy of the adjustment of data to a distribution of probability was evaluated by Kolmogorov–Smirnov or Anderson– Darling, using @Risk software. Seasonal distribution of water temperature was obtained by sampling data generated by the bivariate normal distribution with a mean (μ), a standard deviation (σ) and correlation (see Table S1 in the supplementary data). The parameters of the distributions were estimated by using the method of moments (Montgomery and Ranger, 2003). Samples from the fitted distributions were used to characterize the seasonal distribution of water temperature, through the years, in the simulations for the calculation of uncertainty on risk of illness with the proposed model. 2.2.1.2. Relationship between the density of V. parahaemolyticus in oysters and parameters of seawater. The relationship between the density of V. parahaemolyticus in oysters and the seawater temperature and salinity was quantified by using data from a previous study of the authors, where the reported incidence of detectable V. parahaemolyticus in the tested oyster samples was 99.2% (Sobrinho et al., 2010). The uncertainty of the parameters of regression equation on the estimation of the risk of the occurrence of illness was implemented by using a multivariate distribution of values of parameters. This approach was used because of the strong correlation (r = 0.99) between the parameters α and β of the linear regression model presented in Table 1.

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Table 1 Variables and parameters of the quantitative risk assessment model for Vibrio parahaemolyticus in raw oysters in Sao Paulo State, Brazil. Module

Variable

Description

Distribution/model

Category

Source

Harvest

Twi

Distribution of water temperature (°C)

Input Uncertainty

Oceanographic Institute

Vpth

Density of total Vpb at harvest [log(MPN/g)]

Spring: Normal(24.11; 1.70) Summer: Normal(27.02; 1.01) Fall: Normal(23.98; 2.12) Winter: Normal(20.34; 1.05) −0:944 þ 0:175  Twi þ Normalð0; 0:971Þ

Output

Sobrinho et al. (2010)

Vpph tc tout Tai

Density of pathogenic Vp at harvest [log(MPN/g)] Distribution of collection time (hour) Distribution of time out of water (hour) Distribution of air temperature (°C)

μpbd

Growth rate adjustment factor

Vppbd

Density of pathogenic Vp before depuration [log(MPN/g)] Effect of depuration process Density of pathogenic Vp after depuration [log(MPN/g)] temperature transport to retail (°C) Time transport to retail (hour) Growth rate adjustment factor to retail

Postharvest

D Vppad Tr tr μr

Consumption

a b

Vppr μ retail t retail Vpcons W N

Density of pathogenic Vp at retail [log(MPN/g)] Inactivation rate at retail Retail storage time (hour) Density of pathogenic Vp at consumption [log(MPN/g)] Distribution of average of weight of oysters (grams) Oysters consumed per serving (grams)

Vping

Pathogenic Vp consumed (MPN)

Pill

Probability of illness per servinga

Vpth + log10[Beta(2;2243)] Pert (2;4;8) Uniform(1; tc) Spring: Normal(−1.48; 2.09) + Twspring Summer: Normal(−1.98; 2.02) + Twsummer Fall: Normal(−1.96; 2.32) + Twfall Winter: Normal(−1.49; 2.47) + Twwinter IF(μ max N 0; (60 × μ max)/[Triangular(3;4;5)]; 0), Where: μ max ¼ ½0:00583797  ðTai −5:5Þ
2  1−eð0:34Tai −15:844Þ 

Output Input Input Input

Vpph + Minimum(7 − Vpph; μ pbd × tout)

Output

Discrete[(Normal(−0.72;0.38): Normal(0.03; 0.35)); 1:1] Vppbd + D

Input Output

Logistic(19.9; 2.1) Discrete(20:41; 0.9:0.1) IF(μ max N 0; (60 × μ max)/[Triangular(3;4;5)]; 0), Where: 
2 μ max ¼ 0:00583797  ðTr −5:5Þ  1−eð0:34Tr −15:844Þ IF(Vppad + μ r × tr N 7; 7; Vppad + μr × tr) −0.007 Uniform(4; 120) Vppr – 0.007× Uniform(4; 120)

Input Input Input uncertainty Output Input Input Output

Lognorm(6.86; 2.43) Discrete(6:12; 0.5:0.5)   10Vpcons  W  N  −α Vp 1− 1 þ βing

Input Input

Input uncertainty

Oceanographic Institute

Miles et al. (1997) and Gooch et al. (2002)

Output Output uncertainty

α and β are parameters of Beta–Poisson model (dose–response), characterized as uncertainty parameters (U.S. FDA, 2005). Vp = Vibrio parahaemolyticus.

The effect of this uncertainty on the estimate of the probability of illness was considered in the simulations. For each simulation of the model, a sample of values of the parameters of a set of 1000 values from the uncertainty distributions was obtained by the multivariate normal distribution, using the variance–covariance matrix shown in Table S1.

a normal distribution during summer, winter and spring. Although the fit of normal distribution to autumn data was not as good as the other seasons, the fitted normal distributions were used in the simulation model for all seasons. The parameters of normal distribution for each season are presented in Table 1.

2.2.1.3. Prediction of the distribution of pathogenic V. parahaemolyticus. The incidence of potentially pathogenic V. parahaemolyticus (Kanagawa positive) used in this study was that reported in a previous study by Sobrinho et al. (2010). In the mentioned study, the authors observed that only one (0.8%) oyster sample was positive for pathogenic V. parahaemolyticus and only one isolate (0.044%) among 2243 isolates tested was Kanagawa positive. The ratio between pathogenic and total V. parahaemolyticus densities was assumed to be temperature independent. As detection of only one isolate of pathogenic V. parahaemolyticus among 2243 isolates tested is insufficient to estimate the parameters of Beta distribution by the method of maximum likelihood, a Bayesian approach was used to estimate the distribution of the relative prevalence of pathogenic V. parahaemolyticus in relation to total V. parahaemolyticus. Thus, the relative prevalence of pathogenic V. parahaemolyticus was estimated as a Beta distribution (s + 1; n − s + 1) with s = 1 and n = 2243 (Vose, 2000; FAO/WHO, 2002).

2.2.2. Post-harvest module The inputs for this module are the seasonal distributions of total and pathogenic V. parahaemolyticus at harvest. The growth of V. parahaemolyticus in oysters from harvest to market is influenced by the length of time after harvest, oyster temperature during transportation and efficiency of the depuration process. As the temperature of the oysters during transportation varies considerably, it was assumed that the temperature of the oysters was the same as that detected for the surrounding environment as equilibrium between the two temperatures is established very quickly. Other assumptions were:

2.2.1.4. Predictions of the density of V. parahaemolyticus in oysters at harvest. In order to obtain the predictions for the harvest step, the distribution of seawater temperature and the regression model for total V. parahaemolyticus density (Table 1) were used. The density of log total V. parahaemolyticus in the analyzed samples was found to follow

• The relationship between growth rate of V. parahaemolyticus in oysters and growth rate of V. parahaemolyticus in broth is independent of the temperature. • The growth rate of pathogenic and non-pathogenic strains of V. parahaemolyticus in oysters after harvest is the same. • The average air temperature in Cananeia represents the surrounding environment temperature to which the oysters are exposed after harvest. • The maximum population of V. parahaemolyticus in oysters at any temperature is 7 log10(MPN/g), according to Costa Sobrinho et al. (2011) and Gelli et al. (1979).

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Table 2 Seasonal distribution of mean daily seawater temperature at Cananeia, Sao Paulo, Brazil, between 2001 and 2004. Season

Spring Summer Autumn Winter

Summary statistics water temperature (°C) Mean (μ)a

Mean (σ)b

Variance (μ)

Variance (σ)

Correlation (μ, σ)

24.11 27.02 23.98 20.34

1.70 1.00 2.12 1.05

0.18 0.43 1.02 0.08

0.03 0.09 0.22 0.02

0.82 0.79 −0.81 0.61

Source: Oceanographic Institute, University of Sao Paulo. a μ: Mean daily seawater temperature in a season for a given year. b σ: Standard deviation of the mean daily seawater temperature in a season for a given year.

• The growth of V. parahaemolyticus in oysters in the period between harvest and refrigeration up to internal temperature below 10 °C is not significant. • The shelf life of oysters stored under refrigeration is 5 days, as indicated on the label of oyster packages produced by one company. 2.2.2.1. Growth of V. parahaemolyticus in oysters at post-harvest. It was assumed that the water activity of oysters does not vary significantly after harvest. According to Miles et al. (1997), at an aw in the range of 0.982 to 0.987, the predicted growth rate of V. parahaemolyticus is 0.84 log (CFU) per hour at 26 °C. The same growth rate was assumed to occur in the oysters at the same temperature. The maximum growth rate of V. parahaemolyticus in oysters was considered as a variable of uncertainty modeled as a triangular distribution, with a most probable value of one fourth, minimum of one fifth and maximum of one third of maximum growth rate in broth at 26 °C (U.S. FDA, 2005). Then, 1000 iterations were run and used for quantification of the uncertainty in the risk of occurrence of illness. During analysis of variability of the model, the value considered for the growth rate in oysters was 22% of the maximum value obtained in broth, as observed by Gooch et al. (2002). It was assumed that the growth of V. parahaemolyticus in oysters during exposure to air does not include a lag phase as the changes in the environment are gradual. 2.2.2.2. Conditions of transportation of oyster from the harvest to the depuration plant. Harvested oysters are usually transported to the depuration plant without refrigeration, but protected from the sun most of the time. The period of time between the beginning of harvest and disposition in the depuration tanks was determined based on interviews and timely follow-up with the oyster producers. Results indicated that this period of time depends on the kind of watercraft used, the harvest location and the tidal movement, varying from 2 to up to 8 h, 4 h being the most common. The distribution Pert (2;4;8) was used to model the time of transportation of oysters to the depuration plant (Vose, 2000). Based on information provided by the workers, it was assumed that the time between harvest and disposition in the depuration tanks was the same during the four seasons. 2.2.2.3. Distribution of air temperature in Cananeia, Sao Paulo, Brazil. The data for air temperature were assumed to be representative of the oyster temperature during the transportation from the harvest to the depuration plant, given lack of data on oyster temperature in this step. There is a correlation between the temperature of the air and that of the seawater. This correlation was incorporated in the model by using the distribution of the differences between the temperature of the water and of the air, measured simultaneously. These differences were well characterized by using a normal distribution where parameters were distinct for each season of the year (see Table S2 in the supplementary data). To simplify the model, the distribution of differences between the temperature of the water and of the air was used as a parameter. These differences and the distribution of seawater temperature were

used to determine the distribution of air temperature where the oysters were exposed during the harvesting. The same profile of correlation between the temperature of the air and of the seawater was observed for each season of the year during the analyzed period, and this profile was extended to all the years studied. Although the fit of normal distribution for spring and winter data was not as good as for summer and fall, the fitted normal distributions were used in the simulation model for all seasons (Table 1). 2.2.2.4. Depuration process. In this module, 40 samples of oysters, containing 12 animals each, were used to evaluate two depuration processes used in the two industries located in the Cananeia region: one company uses UV light and the other uses ozone. For each process, 20 samples were collected from the same growing site: 10 samples before depuration and 10 samples after depuration. Enumeration of V. parahaemolyticus was performed by using the Most Probable Number (MPN) technique (Kaysner and DePaola, 2001). Approximately 25 g of the homogenate were added to 225 ml of 3% NaCl (LabSynth, Brazil) solution. Serial 10-fold dilutions were prepared up to 1:106 and three aliquots of each dilution were inoculated into tubes with alkaline peptone water and incubated overnight at 37 °C. A loopful was taken from the top 1 cm of each culture presenting turbid growth and streaked onto thiosulfate citrate bile salts sucrose (TCBS) agar plates (Oxoid, UK) and incubated at 37 °C for 24 h. Five to ten typical colonies from each plate were selected and isolated for biochemical identification using API 20E strips (bioMérieux, France). Due to the limited number of samples analyzed, the non-parametric bootstrap method was selected to characterize the distribution of the effect of the depuration processes. The developed model selects, at random and uniformly, a value of the density of V. parahaemolyticus among the depurated oyster samples and subtracts from other selected value of non-depurated samples, generating a distribution of the difference or of the reduction in the population of V. parahaemolyticus resulting from the depuration process. This procedure of selection was repeated 1000 times, and the distribution of the effect of the depuration process for each plant was inferred. This effect was modeled with a discrete distribution of the effects of the two depuration processes with the same probability of occurrence, called joint probability distribution (Table 3). 2.2.2.5. Data on time of collection transport and storage conditions. The information on the conditions and time of transport was obtained through interviews with people responsible for these steps and also through observations during these activities. The temperature of the oysters during transportation after depuration was monitored during 3 months (August to November), using a data logger (Qualiterm, Brazil) inserted among the oyster packs. The information about the storage conditions was also collected from people responsible for this step. In some cases, it was possible to visit the storage area and measure the temperature by using a digital thermometer (Hanna Instruments, USA). 2.2.2.6. Influence of the conditions of transportation to retail on the density of V. parahaemolyticus. A similarity in the distribution of temperature during transportation by the two oyster companies was observed, so that a common distribution was used for the purpose of simulation. The common distribution was modeled as a fitted logistic distribution with a mean of 19.9 °C and standard deviation of 2.1 °C (Fig. 2). At the end of the depuration process, packages of oysters are stored in ventilated areas or in the delivery vehicles at room temperature. The time for transportation to retail varies from 16 up to 44 h, depending on the distance to be covered. The delivery procedure is the same in the two industries. The effect of the time of transportation was modeled considering that 90% of the oysters produced in Cananeia are sold in the metropolitan area of Sao Paulo, Guaruja, São Vicente and smaller cities in the south seacoast of Sao Paulo State, while 10% are sold in the north

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1000

coast of the State and inland, according to information provided by the oyster producers. Thus, the time of transportation was modeled considering two uniform distributions: one with a minimum time of 16 and maximum of 24 h and probability of 90% and the other with a minimum time of 16 and maximum of 44 h, and probability of 10%.

2.2.3. Consumption module The goal of this module is to get an estimate of the dose of pathogenic V. parahaemolyticus per oysters serving. The data used in the model include the number and weight of oysters in a serving. As the number of oysters in a serving is not known, the best estimate was based on information in the menus of restaurants and beach bars, and on the number of oysters in packages available in the surveyed local markets. In most restaurants and beach bars, the number per serving varied from 6 (most frequent) to 12 (rare) and the packages in the markets contained mostly 6 oysters. Considering the available information and aiming at simplifying the model, the following assumptions were made: the number of oysters per serving is well represented by a discrete distribution between 6 and 12, with equal probability of occurrence; the oysters are stored under refrigeration in restaurants and homes; the population of V. parahaemolyticus in the oysters does not change under the conditions of time and temperature during transportation from the market to the restaurants and homes. The weight of one oyster was estimated by weighing the muscle and intervalve liquid of samples collected at consumption points and at the depuration sites. The distribution of weight was obtained from 91 samples, each sample containing 12 oysters, summing up a total of 1092 oysters, modeled by a lognormal distribution with parameters 6.9 and 2.4 (Costa Sobrinho et al., 2011). Table 3 Predicted distribution of reduction of population of Vibrio parahaemolyticus in oysters submitted to depuration processes. Depuration process

Ozone Ultraviolet light Joint distribution a

Parameters of distribution [log10(MPN/g)] Mean

Standard deviation

CI (95%)a

−0.7 0.0 −0.3

0.4 0.3 0.5

(−1.4; −0.1) (−0.5; 0.6) (−1.2; 0.5)

CI (95%) = Confidence interval of 95% to the average.

Company A Company B

800

Frequency

2.2.2.7. Influence of the oyster storage conditions at retail on the density of V. parahaemolyticus. The temperature of storage of oysters at sampled retail places varied from 6 to 12 °C. Gooch et al. (2002) observed a reduction of 0.003 log10(CFU/g) per hour in the density of V. parahaemolyticus in oysters when stored at 3 °C for 14 to 17 days. Johnson and Liston (1973) reported a reduction of 0.005 log, per hour, in oysters stored at 5 °C for 14 days. Muntada-Garriga et al. (1995) observed a reduction of 0.018 log per hour in the density of V. parahaemolyticus inoculated in a mixture of oysters during 1 week at 4 °C, while Cook et al. (2002) estimated a reduction in 7% per day in oysters at retail level when 21% of the time of the refrigeration temperature exceeded 7.2 °C. Considering that the temperature at retail level is above 5 °C most of the time, there is potential for growth of V. parahaemolyticus in oysters during storage. However, in the absence of exact data of temperature during storage at retail, it was assumed that the temperature does not exceed 10 °C. Thus, it was assumed that a 0.007 log per hour reduction in the density of V. parahaemolyticus is expected during the storage of oysters at 5 °C to 10 °C, correspondent to a mean value from data reported by the cited authors. The time of storage until consumption was modeled considering a uniform distribution with a minimum of 4 and a maximum of 120 h. The minimum time of consumption of 4 h was estimated based on the shortest time between the beginning delivery and consumption (lunch time). The maximum time corresponds to the estimated shelf life.

73

600

400

200

0 5

10

15

20

25

30

35

40

Transport temperature of oyster to retail ( ° C) Fig. 2. Distribution of temperature during transport of oysters to retail for both companies.

2.3. Hazard characterization Due to the lack of data in Brazil, the dose–response model used in this work was the same Beta–Poisson model used by FDA (U.S. FDA, 2005), including the distribution of uncertainty of parameters alpha and beta. The distribution of uncertainty for the probability of disease was determined by running 500 simulations of 5000 iterations each. Each simulation was performed with different values for the dose– response parameters randomly selected from the parameters of uncertainty distribution. The set of estimated means for the probability of illness per serving, obtained with simulation, was used to characterize the distribution of uncertainty on the risk of a person to become ill. 2.4. Risk characterization The dose–response function was combined with the output of exposure assessment to estimate the probability of illness per serving of raw oysters. Sensitivity analysis, crude and advanced (Vose, 2000), was performed to identify and quantify the relative importance of variables of the model on the likelihood of occurrence of illness. 2.5. Statistical analyses The statistical analyses were performed by using SAS (SAS Institute Inc., Cary, NC, USA, v. 9.0), Microsoft® Excel and @Risk softwares (Palisade Corporation, version 4.5), with a significant level of 5% of probability. The model was developed by using a Microsoft® Excel spreadsheet. All simulations were run by using the Latin Hypercube sampling method of input variables and combining the values properly in order to generate the output variables. The simulations were implemented by using the @Risk software. The selection of new values of parameters of uncertainty distributions was implemented through a macro, developed in a “Visual Basic” language in the Microsoft® Visual Basic (Microsoft Corporation, USA). 3. Results and discussion 3.1. Exposure assessment 3.1.1. Prediction of the density of V. parahaemolyticus in oysters at harvest Table S3 (supplementary data) shows the distribution of the abundance of V. parahaemolyticus in oysters at harvest level, for each season, with 5000 iterations. The densities of V. parahaemolyticus during spring and autumn were similar. The mean distribution of seawater temperature in the two seasons was similar as well. The highest population

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Fig. 3. V. parahaemolyticus population in retail oysters compared to the estimated by the developed model with 500 simulations of 5000 iterations (The error bars indicate a standard deviation below and above the average.).

abundance occurred during the summer and was 1.2 log higher than the average detected during winter reflecting the importance of the water temperature as primary driving factor to predict the presence of V. parahaemolyticus in oysters. Other factors such as turbidity and chlorophyll concentration may also be important (Johnson et al., 2010) but no quantifiable data were available to include these factors in the current model. The data indicate that the density of pathogenic V. parahaemolyticus in oysters is overestimated, since pathogenic V. parahaemolyticus was frequently non-detectable in oyster collected in the Cananeia region (Ristori et al., 2007; Sobrinho et al., 2010; Costa Sobrinho et al., 2011), despite the high incidence and high densities of total V. parahaemolyticus in oysters in this region (Gelli et al., 1979; Ristori et al., 2007; Sobrinho et al., 2010). 3.1.2. Influence of depuration processes on the density of V. parahaemolyticus in oysters The results in Table 3 show that the depuration processes used in the processing plants did not reduce the density of V. parahaemolyticus in the oysters. Although there are studies showing that depuration is effective in removing many fecal bacterial contaminants from oysters, current commercial practices are not consistently effective, or are ineffective, in removing other contaminants such as naturally occurring marine vibrios, such as V. parahaemolyticus and V. vulnificus (Lee et al., 2008).

3.1.3. Distribution of densities of V. parahaemolyticus in oysters at the consumption level The effects of processing and post-harvest manipulation on the population of V. parahaemolyticus in oysters are presented in Table S4 (supplementary data). When comparing these predicted populations with those detected in 74 samples at harvest (Costa Sobrinho et al., 2011) some differences can be seen. The model overestimates the populations by 0.7 log10(MPN/g) in oysters during summer, by 0.5 log10(MPN/g) during autumn and 0.6 log10(MPN/g) during the winter. On the other hand, only considering the variability of the model, the differences decrease to 0.5 log10(MPN/g) in summer, 0.2 in autumn and 0.4 in winter, when estimated by a simulation with 10,000 iterations, using the best estimates obtained for the parameter of the variables. The discrepancies are larger when considering the distribution of the population of pathogenic V. parahaemolyticus since at post-harvest module there was no isolate showing the genes tdh or trh. In this case, V. parahaemolyticus populations are overestimated in more than 1 log10(MPN/g). The highest population of total V. parahaemolyticus detected in the tested oyster samples was 6 log 10(MPN/g). These results are similar to those previously reported by Gooch et al. (2002) and Cook et al. (2002), who observed that the maximum populations of V. parahaemolyticus in samples collected at retail level were 5.8 log10(CFU/g) and 6.3 log10(MPN/g) respectively. Assuming that the maximum population of total V. parahaemolyticus corresponds to the maximum value found in the samples analyzed in the laboratory, i.e., 6 log10(MPN/g) instead of 7 log10(MPN/g), the differences between the geometric mean values, estimated by the model and the ones detected by samples, are reduced to 0.2 log10(MPN/g) in summer, 0.1 log10(MPN/g) in autumn and 0.5 log10(MPN/g) in winter (Fig. 3). However, in relation to pathogenic V. parahaemolyticus populations, the difference between the values predicted by the model and the detected ones decreases, but remains around 1 log10(MPN/g), as shown in Fig. 3. In general, the geometric mean density of total V. parahaemolyticus predicted by the model is similar to the one obtained in samples collected at retail level (Costa Sobrinho et al., 2011). The difference between the two values is less than 0.5 log10(MPN/g). 3.2. Modeling in the stage of consumption Table 4 shows the estimated seasonal distribution of V. parahaemolyticus populations, per serving at consumption level, calculated based on 500 simulations of 5000 iterations using the Latin Hypercube method. These results were obtained by multiplying the

Table 4 Predicted population (MPN) of total and pathogenic Vibrio parahaemolyticus, per serving at the moment of consumption. Season

Assumed maximum population [log(MPN/g)]

Total V. parahaemolyticus per servinga

Pathogenic V. parahaemolyticus per servinga

Spring

7

1.4 × 108 (9.5 × 107; 1.9 2.4 × 107 (1.9 × 107; 3.0 1.9 × 108 (1.4 × 108; 2.5 3.1 × 107 (2.5 × 107; 3.6 1.3 × 108 (8.9 × 107; 1.9 2.4 × 107 (1.8 × 107; 3.0 8.1 × 107 (5.0 × 107; 1.2 1.6 × 107 (1.2 × 107; 2.1

1.2 × 105 (8.3 × 104; 1.7 2.2 × 104 (1.7 × 104; 2.7 1.7 × 105 (1.2 × 105; 2.2 2.7 × 104 (2.2 × 104; 3.2 1.2 × 105 (8.0 × 104; 1.7 2.1 × 104 (1.6 × 104; 2.7 7.2 × 104 (4.5 × 104; 1.1 1.4 × 104 (1.0 × 104; 1.9

6 Summer

7 6

Autumn

7 6

Winter

7 6

a

× 108) × 107) × 108) × 107) × 108) × 107) × 108) × 107)

Values in parentheses represent the 5° and 95° percentile, respectively, of the uncertainty distribution.

× 105) × 104) × 105) × 104) × 105) × 104) × 105) × 104)

P.S. Costa Sobrinho et al. / International Journal of Food Microbiology 180 (2014) 69–77 Table 5 Predicted arithmetic mean risk per serving associated with the consumption of raw oysters contaminated with Vibrio parahaemolyticus, harvested and marketed at the state of Sao Paulo, Brazil. Season

Spring Summer Autumn Winter

Assumed maximum population [log(MPN/g)] 7 6 7 6 7 6 7 6

Mean risk per servinga Uncertaintyb

Mean 2.6 4.7 3.6 6.0 2.6 4.7 1.5 3.1

× × × × × × × ×

−3

10 10−4 10−3 10−4 10−3 10−4 10−3 10−4

(2.4 (4.7 (3.3 (5.8 (2.5 (4.4 (1.4 (2.8

× × × × × × × ×

10−4; 7.2 10−5; 1.3 10−4; 9.9 10−5; 1.6 10−4; 7.2 10−5; 1.3 10−4; 4.3 10−5; 8.5

× × × × × × × ×

10−3) 10−3) 10−3) 10−3) 10−3) 10−3) 10−3) 10−4)

a

Obtained from 500 simulations, with 5000 iterations per simulation. Values in parentheses represent the 5° and 95° percentile, respectively, of the uncertainty distribution.

75

10− 6) and New Zealand (8.6 × 10− 8 to 3.2 × 10− 7) but similar to that in Japan during autumn (1.2 × 10− 4) (FAO/WHO, 2011). Unlike the previous studies on estimated risks, this model uses local data on V. parahaemolyticus density in oysters as a function of water temperature, distribution of oyster weight and the rate of pathogenic V. parahaemolyticus in relation to total V. parahaemolyticus. The risk of becoming ill due to consumption of raw oysters contaminated with V. parahaemolyticus detected in this study is similar to that reported for other types of seafood, such as bloody clams in Thailand (Yamamoto et al., 2008) and horse mackerel in Japan (Iwahori et al., 2010), where the estimated risks were 5.6 × 10−4 and 5.6 × 10−6 to 1.4 × 10−4/person/year, respectively. However, the estimated risk was higher than that estimated for cooked shrimps in Malaysia (Sani et al., 2013), which was 4.8 × 10−6/person/year.

b

weight distribution per oyster serving by the population distribution of V. parahaemolyticus at the time of consumption. 3.3. Characterization of the risk of disease caused by V. parahaemolyticus due to consumption of raw oysters 3.3.1. Probability of illness per serving of raw oysters Table 5 shows the estimated risk of illness per serving for each season of the year. The probability of occurrence of illness is higher during summer. The estimated risk per serving obtained by using the model could not be validated due to the lack of epidemiological data in the country. The mean risk per serving in summer is 2-fold higher than the mean risk in winter, and 1.3-fold higher than the mean risk during spring or autumn. The average risks during fall and spring are the same, similar to the observed temperature profile. The model shows that one way to reduce the risk of illness is to improve the conditions of transport of the oysters from the industry to the market using proper refrigeration, as this is the critical step for the growth of V. parahamolyticus. Table 6 compares the estimated risks of illness by V. parahaemolyticus, associated with raw oysters obtained in this work and those reported by FDA for oysters from the Gulf Coast in the United States (U.S. FDA, 2005), where the environmental conditions are similar to those observed in Cananeia. A similarity in the mean estimated risks of illness can be observed, especially during summer and spring, when the difference in the average temperature of seawater is lower. In contrast, the FDA risk assessment estimated that the prevalence of pathogenic strains in the total population of V. parahaemolyticus was higher (0.18%) than that estimated in this study (0.089%, Bayesian statistics), with the median of the fitted Beta distribution closer to raw observation (0.045%). In the evaluation of exposure, the mean density of total V. parahaemolyticus per serving at the moment of consumption estimated by FDA was, approximately, 10 to 100 times smaller than the one estimated in this research. It should be noted that in our study the distribution of percentage of pathogenic V. parahaemolyticus and the weight per consumed oyster per person were lower. The estimated risk in this study is higher than that reported in Australia (6 × 10− 8 to 6.1 × 10− 6), Canada (7.5 × 10 − 10 to 1.1 ×

3.4. Sensitivity analyses of the model Sensitivity analysis was performed to identify and quantify the relative importance of variables of the model for the likelihood of occurrence of illness. Using the crude sensitivity analysis (Fig. 4), the variables that most influenced the risk of illness were the abundance of total V. parahaemolyticus at harvest, the temperature of transportation to retail, the relative prevalence of pathogenic V. parahaemolyticus, the effect of depuration process, the time of storage at retail, the amount of oysters consumed and the time between harvest and arrival to the depuration plant. The seasons did not significantly affect the likelihood of occurrence of illness. Using an advanced technique of sensitivity analysis (Vose, 2000) (Fig. 5), it was observed that among the variables identified by using the crude sensitivity analysis, the relative prevalence of pathogenic V. parahaemolyticus, the temperature of transportation to the retail level, the density of total V. parahaemolyticus at harvest and the time of storage at retail were the most influential variables. Abundance of total V. parahaemolyticus at harvest, temperature of transport and relative prevalence of pathogenic V. parahaemolyticus are the variables positively correlated with the risk of illness. They can increase the risk up to 1000 times, for instance, during summer. The time of storage in refrigeration is the variable that has a negative correlation with the risk of illness per serving. Among the variables that most influence the estimated risk of illness, two are related to post-harvest: temperature of transport and storage time. These two parameters can be managed by the adoption of simple measures and training of personnel. The adoption of refrigeration during transport of oysters to retail would result in a 95–99% reduction in the estimated risk. This work shows that the abundance of V. parahaemolyticus in oysters is higher at the consumption level than at the harvest level indicating that the current practices are not enough to avoid growth of V. parahaemolyticus in oysters along the production chain. Besides, the growth of V. parahaemolyticus in oysters can be influenced by several factors that were not considered in the model, for example, the competition with other microorganisms that can reduce the growth rate of V. parahaemolyticus, the change in intrinsic factors such as pH, physiological state. Another point to be considered is the maximum population density of V. parahaemolyticus observed in oyster, i.e., the population

Table 6 Comparison of calculated risks of illness by V. parahaemolyticus, associated with raw oysters, obtained in this work and reported by FDA for the Gulf Coast (U.S. FDA, 2005), according to the season. Season

Cananeia, Sao Paulo, Brazil Mean risk per serving

Spring Summer Autumn Winter

4.7 6.0 4.7 3.1

× × × ×

10−4 10−4 10−4 10−4

Gulf Coast, USA Mean seawater temperature (°C)

Mean risk per serving

24.1 27.0 24.0 20.3

1.7 4.4 4.3 2.1

× × × ×

10−4 10−4 10−5 10−6

Mean seawater temperature (°C) 24.5 28.9 17.9 14.2

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P.S. Costa Sobrinho et al. / International Journal of Food Microbiology 180 (2014) 69–77

Appendix A. Supplementary data Supplementary data to this article can be found online at http:// dx.doi.org/10.1016/j.ijfoodmicro.2014.04.008.

References

Fig. 4. Influence of variables of the model of risk assessment on the probability of illness per serving of oyster during summer.

abundance reached by the microorganism in the stationary phase of the growth curve. The evaluation of the populations of V. parahaemolyticus in oysters along the production chain and the possible simulated scenarios used for the development of the quantitative model are useful for a systematic evaluation of strategies to minimize the impact on public health. This is valid not only for V. parahaemolyticus but also for other vibrios, such as V. cholerae and V. vulnificus. Further development in the epidemiological surveys and improvements on generation of surveillance data in Brazil will be necessary for a future validation of the model developed in this study. Acknowledgments This study was supported by the Conselho Nacional de Pesquisa Cientifico e Tecnológico (CNPq) and Fundação de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP). We are grateful to FAPESP for providing the Ph.D. scholarship to the first author and to John Bowers, Ph.D. and Jeff Farber, Ph.D., for their valuable comments on the manuscript.

Fig. 5. Effect of the main variables of the model for risk assessment on the probability of developing illness per serving of oyster consumed during winter (A) and summer (B).

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