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A Critical Review of Risk Assessment Models for Listeria monocytogenes in Seafood

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Abstract
A review of the published quantitative risk assessment (QRA) models of L. monocytogenes in seafood was performed, with the objective of appraising the effectiveness of the control strategies placed along the food chain. Whereas all the 13 QRA models retrieved were of short scope, covering at most from end of processing to consumption; the majority (85%) focused on smoked or gravad fish. Since the modelled pathways commenced with the packaged product, none of the QRA models addressed cross-contamination events. Many models agreed on the fact that keeping the product’s temperature at 4.0-4.5 ºC leads to greater reductions in the final risk of listeriosis than reducing the shelf life by one week; and that the effectiveness of both measures can be surpassed by reducing the initial occurrence of L. monocytogenes in the product (at the end of processing). It is therefore necessary that future QRA models for RTE seafood contains a processing module that can provide insight into intervention strategies that can retard L. monocytogenes growth, such as the use of bacteriocins, ad-hoc starter cultures and/or organic acids, and other strategies seeking to reduce cross-contamination at the facilities, such as stringent controls for sanitation procedures. Since risk estimates were shown to be moderately driven by growth kinetic parameters, namely, the exponential growth rate, the minimum temperature for growth, and the maximum population density, further work is needed to reduce uncertainties about them.
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Subject: Biology and Life Sciences  -   Animal Science, Veterinary Science and Zoology

1. Introduction

Ready-to-eat (RTE) foods that are not heat treated or do not undergo any listericidal treatment before consumption are of significance in the transmission of foodborne listeriosis (EFSA BIOHAZ [1]). Recently, using a generic quantitative risk assessment model, EFSA [2] compared the probability of listeriosis in the European Union elderly population linked to products such as RTE fish, pâté, cooked meats, sausages, soft and semi-soft cheeses and blanched frozen vegetables. They found out that gravad fish in normal atmosphere packaging, and hot-/cold-smoked fish in reduced-oxygen packaging (ROP) ranked as the top-most high risk products. The occurrence of Listeria monocytogenes in RTE products have been responsible for outbreaks and product recalls in the EU and the USA. For instance, as reported by the Rapid Alert System for Food and Feed (RASFF), over 40% of the seafood notifications between 2008 and 2016 were related to L. monocytogenes [1]. In terms of reported outbreaks, according to EU surveillance data [3], in the period between 2010 and 2020, fish and fish products (namely, crab meat, crustaceans, shellfish and mollusks, smoked fish and non-specified seafood) caused 23% of the 53 strong-evidence outbreaks in the EU. Unlike the EU scenario, in the USA during the ten-year span, fish and fish products (namely, smoked fish and raw sushi) had a lower share of 6% of the 50 strong-evidence outbreaks [4]. Considering that most of the listeriosis cases take place sporadically [1,5], results of a recent meta-analysis on case-control studies of sporadic listeriosis (Leclercq et al. [6]) cannot be overlooked. Combining the odds ratio (OR) outcomes from 12 primary studies, Leclercq et al. [6] found that RTE seafood presented the highest association with sporadic listeriosis, with pooled OR=10.95 (p<0.001) for non-perinatal population, and pooled OR=6.273 (p<0.001) for all susceptible population (in comparison to processed meats, cheese, vegetables, fruits and composite foods).
Various listeriosis quantitative risk assessment (QRA) models have been produced for RTE seafood [7] in an attempt to provide guidance for reducing the occurrence of L. monocytogenes by practices or strategies that retard or prevent the growth of this pathogen. The objectives of this study are: (i) to undertake a critical review of the published QRA models of listeriosis acquired from the consumption of RTE seafood; (ii) to contrast the control measures or strategies evaluated in the various QRA models as what-if scenarios; and (iii) to derive important lessons and recommendations for future QRA models in RTE seafood.

2. Materials and Methods

QRA models were retrieved through a literature search on Scopus and PubMed® considering 1998 as the starting year of publication. The searches in title, keywords and abstract were carried out on 18 May 2022, using logically connected terms ((“risk assessment” OR exposure OR quantitative microbial OR risk modelling OR modeling OR simulation* OR second-order OR “second order” OR “risk management”) AND (“L. monocytogenes” OR “Listeria monocytogenes” OR listeriosis)) properly arranged in the syntaxes of the literature search engines. The full systematic review process and extraction of information have been described in Gonzales-Barron et al. [7]. The present review focuses only on seafood products, which were the subject of 13 QRA models described in 14 publications [1,5,8,9,10,11,12,13,14,15,16,17,18,19].

3. Results

A total of 13 QRA models on seafood as source of listeriosis were recovered in the literature search of models published between January 1998 and May 2022. Table 1 compiles the main features of the 13 QRA models; whereas Table 2 summarises the predictive microbiology models and main outcomes related to what-if scenarios and sensitivity analysis.
None of the 13 QRA models comprised simulations of cross-contamination neither one single QRA model included a processing stage module. All the models represented short supply chains; this is, from end of processing or retail to table, or consumption only. Three QRA model assessed the growth of L. monocytogenes from the end of processing until consumption [8,9,10,11], and other three began the analysis from retail [1,5,12]. Seven out of 13 models represented solely the consumption module [13,14,15,16,17,18,19].
Most of the models focused on RTE seafood, except for one [18], which dealt with a traditional processed fish from Ghana. Within the RTE seafood, the majority of models (75%) estimated the risk of listeriosis from smoked fish (or salmon) while the other 25% the risk associated with gravad fish. In addition, other products were investigated by development of QRA models such as raw seafood, preserved fish and cooked RTE crustaceans by FDA-FSIS [5] and salt-cured fishery products by Pasonen et al. [19].
More than half of the available models (9/13) represented the conditions of European countries, namely France [8,9,10], Finland [19], Ireland [17], Sweden [13] and Spain [15] – where these products are highly consumed – in addition to models populated with data from the EU [1,12]. Two QRA models pertained to the risk of listeriosis in the USA population [5,11], whereas two models were not linked to any specific geographical location [14,16] (Table 1).
The QRA models retrieved varied in the degree of complexity of their construction. All QRA models, except two [13,18], employed predictive microbiology models, including microbial growth, survival, competition models and die-off and re-growth models (Table 2). Lag phase duration of L. monocytogenes was approached in 5 models [11,12,14,16,17]; whereas only 3 QRA models [8,9,10,12] employed time-temperature trajectories to more realistically estimate the kinetics of L. monocytogenes during cold storage and shelf life (Table 1).
Whereas one model did not perform any risk estimation as authors targeted the estimation of the regulatory and recall risk [11], the other twelve models considered illness as end-point for risk estimation. The exponential dose response function was the equation of choice for risk characterisation in 9 QRA models, although they followed different approaches, namely the dose-response models of Pouillot et al. [20] (used by Pérez-Rodriguez et al. [12], and as modified versions by Fritsch et al. [10] and EFSA-BIOHAZ [1]), of FAO-WHO [14] (used by themselves [14] and Garrido et al. [15]), of FDA-FSIS [5] (used by themselves), of Pasonen et al. [19] (used by themselves) and of Buchanan et al. [21] (used by Lindqvist and Westöö [13]). The early Weibull-gamma model proposed by Farber et al. [23] was used in the QRA models of Dass [17] and Bomfeh [18], whereas the beta-Poisson model of Haas et al. [22] was employed in the QRA model of Gospavic et al. [16] (Table 1).
Except for Bomfeh [18], all seafood QRA models assessed the effect of what-if scenarios on the final risk; whereas sensitivity analysis on response variables such as L. monocytogenes concentration at consumption, regulatory and recall risk, or final risk measures was undertaken in 38.0% of the models [1,8,9,11,13,17] (Table 2)

4. Discussion

Listeriosis QRA models in seafood focused mainly on RTE smoked and gravad fish (i.e., generic fish, salmon or trout), because these are products of considerable public health implications of listeriosis since (1) they are not heat-treated; (2) are given a relatively long shelf-life; (3) are mostly vacuum-packed – which does not preclude L. monocytogenes growth; and (4) are generally eaten with no prior cooking. Many reports and surveys have indicated that seafood products are frequently contaminated with L. monocytogenes [24]. A recent genomic-based epidemiological study [25] determined that, from 2018 to 2020, 27% of all listeriosis cases in Germany with suspected food vehicles were caused by smoked or gravad salmon products. These authors demonstrated that despite the considerable effort put to improve the safety of smoked fish, outbreaks linked to the presence of L. monocytogenes at infective levels in these seafood products continue to happen. Control of L. monocytogenes in smoked fish is challenging because this pathogen is widely distributed in a variety of environments, including natural ones [26,27] and processing facilities [28]. This underscores the need for assessing new technological interventions, post-lethality treatments, and intensified sanitation programmes to reduce the risk of listeriosis in RTE seafood.

4.1. Risk Factors at Processing

Apart from the L. monocytogenes contamination in raw materials (fish) as primary source, smoked fish can acquire the pathogen from food contact surfaces in the processing environment. Moreover, the fate of L. monocytogenes is variable along the processing steps of evisceration and filleting [29,30], brining [31], smoking [32] and slicing [33]. Nevertheless, despite extensive data availability, none of these effects has been simulated in the seafood QRA models retrieved. The first QRA model of broadest scope was that of Pouillot et al. [8,9] which followed the cold smoked salmon supply chain from the end of processing until consumption. Such a model innovated in the assumptions of: (1) a Jameson effect to account for the inhibitory effect of the background microflora on L. monocytogenes in the product within the vacuum-package atmosphere; and (2) the use of dynamic time-temperature profiles to represent realistic temperature oscillations between cold storage at the end of processing and home refrigeration. Other two QRA models represented the supply chain from the end of processing: Fritsch et al. [10] model, which has the same structure as Pouillot et al. [8,9] model, but refined by the introduction of phenotypic growth characteristics of L. monocytogenes by subgroup and virulence properties; and Chen et al. [11] model, which did not proceed to the risk characterisation stage.
In relation to the assessment of post-lethality treatments such as product reformulation involving applications of lactate or diacetate, nisin, or specific starter cultures; these were not assessed in any of the QRA models retrieved, except in Chen et al. [11], where they pointed out that the addition of nisin (5-20 ppm) is far more effective than the addition of 2% potassium lactate plus 0.14% sodium diacetate in decreasing the overall risk of a sampled lot found positive for L. monocytogenes (67-95% reduction versus 6% reduction, respectively). Although QRA models of Pouillot et al. [8,9], Fritsch et al. [10], Pérez-Rodríguez et al. [12] and Gospavic et al. [16] were equipped with Jameson-effect models able to characterise the inhibitory effect of lactic acid bacteria on L. monocytogenes, none of these QRA models evaluated scenarios relative to the addition of ad-hoc cultures of lactic acid bacteria (Table 2). Although the cold smoked fish model of FAO-WHO [14] did not employ any Jameson-effect competition model, still it considered the effect of indigenous lactic acid bacteria, assuming that at high concentrations, they can suppress the growth of L. monocytogenes. This model made its point demonstrating the ability of cold smoked fish to support the growth of L. monocytogenes, which even under the optimistic assumption that the growth rate inhibition of L. monocytogenes due to the growth of lactic acid bacteria is of 95%, the listeriosis cases per thousand people would be ~70 fold over what the cases would have been if cold smoked fish did not support the growth (Table 2).

4.2. Cross-Contamination in Processing Plants

Abundant literature has demonstrated that cross-contamination can occur at the different processing stages of RTE seafood. During head cutting, evisceration and filleting, there are many opportunities for L. monocytogenes to be transferred from the exterior of fish to cut surfaces of fillets or sides [29,34]. At this stage of processing, flesh areas of fish can be inoculated by contact with contaminated skin sides of fillets, filleting tables and knives and gloves. For instance, Dass [17] detected L. monocytogenes types c and b on filleting boards, deboning pins, conveyor belts and knives. In another study, Chen et al. [30] monitored contamination of L. monocytogenes in catfish fillets and in environmental samples collected from various areas of the processing plant. They isolated serotype 1/2b (47%) from trimming boards, conveyor belts and holding tables; and found that conveyors were contaminated with L. monocytogenes 16.6% of the sampling times (6/36). In a processing facility of gravad salmon in Brazil, Cruz et al. [35] detected 80% of positive swabs of L. monocytogenes from handlers, and 25% from knives and tables. Lundén et al. [36] explained that the contamination on tables and cutting surfaces can adhere strongly within a short period of time. This entails the possibility that filleted fish become contaminated during the first stages of processing.
Slicing machines can be regarded as a source of L. monocytogenes contamination [33]. Di Ciccio et al. [37] repeatedly isolated L. monocytogenes serotypes 1/2a and 1/2b from slicer belts, distribution trays, slicing machines and slicing covers for three years in a smoked-salmon production facility. Out of 95 environmental samples tested, slicing machines (37%) and working tables (43%) had the highest frequencies of detection. In the USA, in a processing plant of catfish fillets, Chen et al. [30] determined that 15% (7/45) of the sampling times, skinning, slicing and blending equipment were contaminated with L. monocytogenes.
The review published by Jami et al. [24] showed that drains and floors, as non-direct food contact surfaces, are the most frequently contaminated sources with prevalences of 2-80% and 1-53%, respectively. Drains and floors may therefore represent niches of contamination. Many authors have also encountered that cross-contamination can occur during packaging from surfaces that are in direct contact with the food being packaged. In cold-smoked salmon plants, Autio et al. [38], Vogel et al. [39], Klaeboe et al. [40], Nakamura et al. [41], Thimothe et al. [42] and Hu et al. [43] recovered L. monocytogenes from direct food contact surfaces in the packaging equipment at the frequencies of 20/84, 140/818, 23/155, 9/101, 6/125 and 5/344, respectively.
Nonetheless, despite actual environmental contamination in processing plants, none of the 13 QRA models assembled cross-contamination modules that could help assess the effects of implementing more stringent controls for environmental monitoring programmes, good manufacturing practices, and sanitation standard operation procedures.

4.3. Shelf-life and Risk Factors at Retail and Home

As the seafood QRA models were of short scope, the typical scenarios assessed were those related to reductions of L. monocytogenes initial prevalence/concentration, storage temperature, time of consumer storage, and shelf-life (Table 2). Vacuum-packaging is widely used in the smoked/gravad fish industry as it delays the proliferation of aerobic spoilage bacteria and minimises oxidative reactions. However, although vacuum-packaging is used to extend the shelf-life of these products, microaerophilic or facultative anaerobic microorganisms, such as L. monocytogenes, may thrive under such condition; and an extended shelf-life may give sufficient time for the pathogen to increase to infective levels. The EFSA BIOHAZ generic QRA model [1] clearly made this point by showing that across three RTE fish products (cold-smoked, hot-smoked and gravad fish), there is no strong difference in the probability of a product exceeding 100 CFU/g at the time of consumption between normal packaging (0.066 – 0.112) and reduced-oxygen packaging (0.040 – 0.115; Table 2). Within this context, many of the QRA models have tested what-if scenarios of shorter time for consumption. Researchers have obtained different estimates of degree of reduction in number of listeriosis cases for smoked fish.
Decreasing the consumption time of unopened packages to a maximum of 7 days at cold storage, led to reductions in the cases of listeriosis of: 80% for cold smoked fish (FAO-WHO [14]; baseline 14 days), 80% for packaged cold-/hot-smoked fish and gravad fish (Pérez-Rodríguez et al. [12]; baseline not clear), 63% for cold smoked salmon (Pouillot et al. [9]; baseline 32 days maximum), 45% for smoked trout (Garrido et al. [15]; baseline 30 days), and 15% for smoked salmon (Garrido et al. [15]; baseline 30 days). Reducing the shelf-life to 14-15 days of cold smoked salmon was estimated to reduce the listeriosis cases in 77% (Pouillot et al. [8,9]; baseline 32 days) and the risk of illness in 64% (Gospavic et al. [16]; baseline 28 days). FDA-FSIS [5] estimated that by reducing the maximum home storage time from 45 to 30 days, the mean cases of listeriosis would be reduced in 38% in the elderly population (Table 2).
Shelf-life reduction therefore appears as a good strategy to decrease the risk of listeriosis; although its effectiveness can be surpassed by effectively maintaining the product at 4.0-4.5 ºC during home storage, as attested in the what-if scenarios of Dass [17], Garrido et al. [15], Pérez-Rodríguez et al. [12] and Pouillot et al. [8,9]. Furthermore, in a sensitivity analysis carried out on the annual risk of illness in the high-risk population linked to smoked salmon, the temperature in the consumer’s fridge was more determinant of risk (r=0.13) than the time in the consumer’s fridge (r=0.06) [17]. A comparable rank correlation coefficient for storage temperature (r=0.177) was estimated by Chen et al. [11]. In a Bayesian QRA model [19], although no comparison was done between a temperature lowering scenario with a shelf-life reduction one, they underscored the importance of maintaining cold temperature at the domestic level, by predicting that if home mean storage temperature decreased from 7 ºC to 3 ºC, the median cases of listeriosis per 100 000 people would decrease in 70% for the elderly population and in 40% for the working-age population (Table 2).
Pasonen et al. [19] model was different from the other models in that it allowed the possibility of continuing consumption of the same (contaminated) package of cold- smoked and salt-cured salmon, rather than assuming independent consumption days; and in that the model was not solved as a forward problem according to Monte Carlo simulation. This QRA model was built on a Bayesian two-state Markov chain approach consisting of three modules: a module for occurrence data, a module for consumption data and a predictive model for the total number of listeriosis in the population. As an Markov-chain Monte Carlo (MCMC) simulation, bottom-up and top-down approaches are combined, and thus all unknown parameters can be jointly estimated from a single compact model. As a result, Pasonen et al. [19] could estimate the uncertainty distribution of parameters, truly reflecting the information contained in the data. Even, the r parameter of the exponential dose-response model was estimated from Finnish data by using the reported number of listeriosis to calibrate the dose-response function for the target populations. Nonetheless, in this calculation, it was assumed that all these listeriosis cases resulted from the consumption of the smoked fish, because the attribution of other sources was not modelled. This fact does not invalidate the MCMC approach proposed by Pasonen et al. [19], since the parameter(s) of the dose-response function could be as well assumed as known. Although it demands a more complex programming, Bayesian inference features many advantages such as: utilising the whole data set jointly as one, handling censored values, taking uncertainty into account, and the possibility to use prior knowledge.
Decreasing the initial mean prevalence or concentration of L. monocytogenes was another of the frequent what-if scenarios, which in most of the QRA models of smoked salmon turned out to be at least as effective in reducing the risk of listeriosis as maintaining the consumer’s fridge temperature to 4 ºC. This was observed in the models of Pouillot et al. [8,9], Chen et al. [11], Pérez-Rodríguez et al. [12], Gospavic et al. [16], Dass [17] and Lindqvist and Westöö [13]; and will be described as follows. In the hypothetical scenario of having the prevalence of contaminated packages of cold smoked salmon decreasing from 1.0 to 0.25, the number of listeriosis cases would drop in 75% (Pouillot et al. [8,9]). Chen et al. [11] estimated that reducing the initial prevalence to half would cause a reduction of 45% in the regulatory and recall risk. Whereas Pérez-Rodríguez et al. [12] predicted that decreasing the maximum initial concentration of L. monocytogenes in 2.0 log would ensure a drop in listeriosis cases in >99.9%, Gospavic et al. [16] estimated that reducing the mean initial concentration from 25 CFU/g to 4 CFU/g would reduce the risk of listeriosis in 67%. In line with the QRA models above – yet, as sensitivity analysis outcomes, Dass [17] also showed that the concentration of L. monocytogenes at retail was a stronger determinant (r=0.97) of the annual risk of illness in the high-risk population than the temperature/time in the consumer’s fridge (r=0.06-0.13). Similarly Chen et al. [11] estimated that the initial contamination level (r=0.404) was a stronger determinant of the regulatory and recall risk than the storage temperature (r=0.177). Without providing coefficients of correlation, Lindqvist and Westöö [13] indicated that the variables impacting on the annual risk of listeriosis, in decreasing order, were: initial counts of L. monocytogenes at retail, prevalence of L. monocytogenes, serving size and proportion of virulent strains (Table 2).
The results of these short-scope QRA models have shown, as a whole, that even when storage temperatures can be kept as low as 3-4 ºC by the consumers, L. monocytogenes can proliferate in smoked/graved fish; but departing from initial contamination or prevalence that are low at the beginning of retail (or end of processing) would heavily result in a lower risk of listeriosis. Therefore, a processing module that is robust enough should be built to represent strategies, combination of strategies and/or sanitation control schemes leading to reductions in the prevalence/concentration of L. monocytogenes.

4.4. Microbial Growth Kinetic Parameters as Drivers of the Final Risk

Interesting outcomes from Pouillot’s QRA model was the high impact of the kinetic parameters of both L. monocytogenes and background microflora on both the concentration of L. monocytogenes in contaminated servings [8] and the listeriosis cases in the reference population [9]. Right after the initial L. monocytogenes prevalence (p=10-20) and the mean temperature at retail phase (p=10-20), the concentration of L. monocytogenes in contaminated servings was highly sensitive to the minimum temperatures for growth of L. monocytogenes (p=10-8) and the background microflora (p=10-6), followed by the reference growth rates at 25 ºC of L. monocytogenes (p=0.015) and the background microflora (p=0.025), and the maximum population density (p=0.008). Likewise, the listeriosis cases in the reference population was heavily impacted by the mean and the standard deviation of the maximum population density (p=10-137; p=10-76); the reference growth rates at 25 ºC of L. monocytogenes (p=10-101) and the background microflora (p=10-8), and the minimum temperature for growth of L. monocytogenes (p=10-12). Likewise, Chen et al. [11] found out, that after the initial contamination level (r=0.404), important determinants of the recall risk were the microbial kinetic parameters of exponential growth rate at 25 ºC (r=0.275) and the minimum temperature for growth (r=-0.169).
The distribution of the minimum temperature for growth was also demonstrated to have an impact on the mean concentration of L. monocytogenes in servings in Fritsch et al. [10] model. The ability to multiply in the cold was correlated with the presence of a genetic marker for cold adaptation. The authors looked for this marker in a collection of strains representative of smoked salmon at the distribution stage [44]. By defining two distributions about the minimum temperature for growth for slow-growing strains and fast-growing strains, Fritsch et al. [10] showed that the mean exposure of the consumer was two times more important in the high growth groups (25 CFU/g) compared to the low growth groups (13 CFU/g). However, the importance of the type of strain on exposure was less significant for the risk of listeriosis than the presence of virulence markers.
In addition to the QRA model of Pouillot et al. [8,9], two other models ascertained that the maximum population density of L. monocytogenes is not a parameter of minor importance: whereas Chen et al. [11] encountered certain association with the risk recall (r=0.053), EFSA BIOHAZ [1] determined that the listeriosis risk was very sensitive to the maximum population density, and quantified that a shift in 0.5 log CFU/g can double the estimated risk (Table 2). In order to obtain more precise estimates of the risk, it is therefore important to reduce the uncertainties about the characteristics of L. monocytogenes, including the parameters associated with the exponential growth rate, the nominal minimum temperature for growth, and the maximum population density.

4.5. Models’ Availabiltity

The most widespread use of risk assessment approaches involves the sharing and description of models [45]. As we have recently shown for other foods [7], most of QMRA models for seafood are not often available. Details of the software used and links describing the models available are provided in Table S1 of the Supplementary Material of this article.

5. Conclusions

Eighty-five percent of the QRA models were carried out for cold-smoked/gravad fish, because of the considerable and continuous public health implications of the seafood products. Despite the availability of L. monocytogenes data on stages such as filleting, brining, smoking and slicing, none of the QRA models contained a processing module; and therefore, cross-contamination events were not represented either. Since secondary contamination can occur in the processing plants from equipment and environmental elements, the most relevant opportunities for cross-contamination during processing should be identified and modelled. Thus, in addition to cold chain (distribution and retail) and consumption – stages that have been represented by most QRA models, a future smoked/gravad fish model should also comprise a processing module, robust enough to allow the assessment of (1) intervention strategies that can retard the growth of L. monocytogenes, such as the application of bacteriocins (nisin), suitable starter cultures and/or organic acids; and (2) control measures that can reduce the frequency of cross-contamination events such as the implementation of more stringent controls for raw materials, environmental monitoring programs and/or sanitation procedures. Given the strong evidence of an inhibitory effect of background microflora on L. monocytogenes in vacuum-packed RTE smoked fish, predictive microbiology models that describe microbial competition should be employed after the stage of reduced-oxygen packaging. Furthermore, since the growth kinetic parameters of L. monocytogenes and microflora have been demonstrated to have a heavy influence on the estimated risk of listeriosis, efforts should be put in accurately modelling such growth kinetic parameters.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org, Table S1: Accessibility to listeriosis QRA published models for seafood products.

Author Contributions

Conceptualization, M.S. and U.G.-B.; methodology, M.S. and U.G.-B.; software, U.G.-B.; validation, V.C., J.O.M., L.G., M.S. and U.G.-B.; formal analysis, M.S. and U.G.-B.; investigation, V.C., L.G., M.S. and U.G.-B.; resources, M.S..; data curation, L.G., V.C., U.G.-B.; writing—original draft preparation, U.G.-B., L.G. and M.S.; writing—review and editing, J.O.M., V.C. and U.G.-B.; visualization, V.C., L.G., M.S. and U.G.-B..; supervision, M.S. and U.G.-B.; project administration, J.O.M. and M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data is contained within the article. The BibTeX file containing records used in this systematic review is available.

Acknowledgments

None.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Features of quantitative risk assessment models of L. monocytogenes from consumption of seafood products by scope.
Table 1. Features of quantitative risk assessment models of L. monocytogenes from consumption of seafood products by scope.
Scope Food RTE Cross-conta-mination DR – End-point Type of DR model DR Sub-populations Strain variability Temp profiles/
Lagtime		 Country Source
End Processing-to-table Cold smoked salmon Yes No Exp – I Pouillot et al. [9] Multiple NA Yes/No France Pouillot et al. [8,9]
Cold smoked salmon Yes No Exp – I Fritsch et al. [10]: r values from Pouillot et al. [20] were re-scaled to three diff-erent groups of virulence (according to CCs) General Specific prevalence for each LM genotypic sub-group (CC) in Europe; Two different distributions for Tmin to represent “low-growing”
and “high-growing” strains; Three virulence levels in the DR r values		 Yes/No France Fritsch et al. [10] (model based on Pouillot et al. [8,9] integra-ting genomic data)
Cold smoked salmon Yes No None NA NA Variable proportion of contaminated packages and growth kinetics parameters according to LM serotypes 1/2a, 1/2b and 4b No/Yes USA Chen et al. [11]
Retail-to-table Various: smoked seafood, raw seafood, preserved fish, cooked RTE crustaceans Yes No Mouse Epi – I FDA-FSIS [5] Multiple Variability in the virulence of different strains represented in DR No/No USA FDA-FSIS [5]
Packaged cold-/hot-smoked fish and gravad fish Yes No Exp – I Pouillot et al. [20] Multiple Challenge test data from a mixture of strains; h0 distribution of variability in physiological state of cells; variability in strain virulence and in susceptibility across population subgroups Yes/Yes EU Pérez-Rodríguez et al. [12]
Cold-, hot-smoked fish, gravad fish Yes No Exp – I EFSA BIOHAZ [1] based on Pouillot et al. [20] Multiple (sex/age group) Challenge test data from a mixture of strains; Strain virulence and host susceptibility explicit in r distribution No/No EU EFSA BIOHAZ [1]
Consump-tion Smoked/gravad salmon/rainbow trout Yes No Exp – I Buchanan et al. [21] General All strains are virulent vs a proportion of virulent strains No/No Sweden Lindqvist and Westöö [13]
Cold smoked fish Yes No Exp – I FAO-WHO [14] High-risk/Low-risk Strain diversity implicit in r No/Yes Non-specific FAO-WHO [14]
Smoked fish and sliced cooked ham Yes No Exp – I FAO-WHO [14] High-risk/Low-risk Strain diversity implicit in r No/No Spain Garrido et al. [15]
Cold smoked salmon Yes No BP – I Haas et al. [22] General NA No/Yes Non-specific Gospavic et al. [16]
VP cold smoked salmon Yes No WG – I Farber et al. [23] High-risk/Low-risk Challenge test data from a mixture of strains; No/Yes Ireland Dass [17]
Traditional processed fish No No WG – I Farber et al. [23] High-risk/Low-risk NA No/No Ghana Bomfeh [18]
Cold smoked and salt-cured fishery products Yes No Exp – I Pasonen et al. [19] High-risk/Low-risk NA No/No Finland Pasonen et al. [19]
DR: dose-response; Exp: exponential model; Mouse-Epi: Mouse-Epidemiological model; I: illness endpoint; D: death endpoint; NA: not addressed in the study.
Table 2. Predictive microbiology models and main outcomes related to what-if scenarios and sensitivity analysis from quantitative risk assessment models of L. monocytogenes (LM) from consumption of seafood products.
Table 2. Predictive microbiology models and main outcomes related to what-if scenarios and sensitivity analysis from quantitative risk assessment models of L. monocytogenes (LM) from consumption of seafood products.
Scope Food Predictive microbiology models What-if scenarios Sensitivity analysis Model complexity Source
End processing-to-table Cold smoked salmon Growth (Jameson effect LM and background microflora, growth square root models for LM and background microflora) EXPOSURE ASSESSMENT: (1) Reducing theoretical shelf-life from 28 days to 15 days reduced mean LM/g in contaminated servings in 10%; (2) The baseline scenario of 21.4% of shelf lives at home longer than 7 days was compared to a scenario of consumption within 7 days maximum; and reduces the mean LM/g in 10%; (3) Better refrigeration at retail from mean temperature 5.6 to 4ºC reduces the mean LM counts in 19%; (4) Better refrigeration at home from mean temperature 7 to 4ºC, reduces the mean LM counts in 36%; (5) Lower initial concentration from 0.46% of values above 1 CFU/g to a distribution truncated at 1 CFU/g, reduces the mean LM counts in 8%.

RISK ASSESSMENT: Output – Listeriosis cases compared to a base 100 for the baseline model: (1) Shelf-life 15 days =23; (2) Prevalence of LM to a quarter=25; (3) Mean home refrigerator temperature 4ºC = 34; (4) Consumed 7 days after purchase = 37; (5) Prevalence of LM to a half = 50; (6) Mean retail temperature at 4ºC=67.		 EXPOSURE ASSESSMENT: Output – concentration of LM in contaminated servings: (1) Total duration at the consumer phase (p=10-30), (2) Mean temperature at the consumer phase (p=10-20), (3) Initial LM counts (p=10-20), (4) Mean temperature at retail phase (p=10-14), (5) Total duration of retail phase (p=10-8), (6) Tmin for growth (p=10-8), (7) Tmin microflora (p=10-6), (8) Initial background flora counts (p=0.002), (9) Serving size (p=0.003), (10) MPD (p=0.008), (11) Ref GR at 25ºC (p=0.015), (12) Ref GR of flora at 25º C (p=0.025).

RISK ASSESSMENT: Output - listeriosis cases in the reference population: (1) r value of DR model (p=10-300), (2) SD(MPD) (p=10-137), (3) Ref of GR 25ºC for LM (p=10-101), (4) MPD of LM (p=10-76), (5) Tmin of LM (p=10-12), (6) GR of flora 25ºC (p=10-8), (7) Prevalence of LM (p=10-6), (8) Servings/year (p=10-2).		 Medium: Complex predictive microbiology model; a new method for solving growth under dynamic temperature profiles was proposed. Pouillot et al. [8,9]
Cold smoked salmon Growth (Jameson effect LM and background microflora, growth square root models for LM and background microflora) Baseline predicted 978 listeriosis cases after consumption of 50 g cold smoked salmon with an initial LM prevalence of 10.4% considering one single prevalence distribution. (1) Taking into account specific prevalences for each LM genotypic sub-group lowered the cases to 574 listeriosis cases; (2) 97% of listeriosis cases were caused by the hypervirulent group despite their low prevalence (12.6%) in contaminated salmon. Inversely, the most prevalent (hypovirulent) group 51.7%) was responsible for only 0.02% of the listeriosis cases; (3) The effect of the low/high growth strains (two distributions for Tmin) was lower than the effect of the virulence: mean exposure from high growth LM group was 25 CFU/g, compared to the low growth groups (13 CFU/g). NA Medium: Same as Pouillot et al. [8,9] but with the further complexity of adding phenotypic characteristics of LM by subgroup and virulence properties of LM. Fritsch et al. [10] (model based on Pouillot et al. [8,9] integra-ting genomic data)
Cold smoked salmon Growth models (Buchanan, Gompertz and Baranyi as primary models, and secondary square root model); and Die-off and re-growth models (Weibull-Buchanan, Weibull-Gompertz and Weibull-Baranyi) End point of the model is the regulatory and recall risk (RRR) defined as the overall risk of a lot sampled found positive for LM. (1) Treatment of salmon with 5 or 20 ppm nisin reduced RRR to 0.109 or 0.017 (in comparison to baseline RRR of 0.333); (2) Reducing prevalence to half decreased RRR to 0.182; (3) Use of inhibitors (2% potassium lactate + 0.14% sodium diacetate) slightly reduced RRR to 0.313; (4) Keeping cold storage below 5ºC did not reduce RRR. Output – regulatory and recall risk: (1) initial contamination level (r=0.404), (2) GR at 25ºC (r=0.275), (3) storage temperature (r=0.177), (4) Tmin (r=-0.169), (5) MPD (r=0.053) Medium: Uncertainty and variability are separated; the die-off and/or growth kinetics are too compartment-alised. Chen et al. [11]
Retail-to-table Various: smoked seafood, raw seafood, preserved fish, cooked RTE crustaceans Growth (linear model, EGR5 square root models) (1) For cold smoked salmon, reducing the max home storage time from 45 to 30 days, reduces the mean cases in 38% in the elderly population. NA Medium:
Various foods		 FDA-FSIS [5]
Packaged cold-/hot-smoked fish and graved fish Growth (Baranyi model with Jameson effect LM and LAB, EGR5 square root model and effect of lactate) (1) Decreasing the maximum initial LM concentration by 2 log decreases listeriosis cases per million servings in >99%; (2) Decreasing time to consumption in 25% decreases listeriosis in 80%; (3) Decreasing 1-2 ºC in the dynamic temperature profiles reduces cases in 75%; (4) Including lag time in the model has no effect on listeriosis cases. NA Medium:
Time temperature dynamic profiles from retail to consumption, and microbial competition models used solved with RK4 algorithm.
 Pérez-Rodríguez et al. [12]
Cold-, hot-smoked fish, gravad fish Growth (Rosso model, EGR 5ºC) (1)Across the 3 RTE fish products, there is no strong difference in the probability of a product exceeding 100 CFU/g at the time of consumption between normal packaging (0.066 – 0.112) and reduced-oxygen packaging (0.040 – 0.115); (2) In both, reduced-oxigen and normal packaging, hot-smoked fish presented higher probability of exceeding 100 CFU/g at the point of consumption (0.115, 0.112) than cold-smoked fish (0.080, 0.074) and gravad fish (0.047, 0.066). Risk is very sensitive to MPD. A shift in 0.5 log CFU/g can double the estimated risk. However, sensitivity analysis was conducted taking together various RTE food classes. Low: Generic model; only demands some knowledge in R software to utilise it EFSA BIOHAZ [1]
Consump-tion Smoked / gravad salmon / rainbow trout NA (1) The minimum level of LM resulting in a risk of illness greater than 10-7 or 10-8 was 25 or 2 CFU/g; (2) If the assumption that all strains are virulent was reduced to 1-10%, the annual listeriosis cases is reduced by 84% in the both high-risk and the low-risk populations. Output – annual risk of illness: ranked as initial LM counts, prevalence, serving size, proportion of virulent strains Low Lindqvist and Westöö [13]
Cold smoked fish Growth (LM growth model affected by LAB growth, Square root model for GR as a function of tempera-ture, pH, aw, un-dissociated lactic acid) (1) If LM growth rate inhibition due to LAB growth is between 80-100%, the increase of listeriosis per 100 000 people is 684-fold in the overall population, in comparison to the baseline scenario of no-growth of LM between purchase and consumption; (2) If LM growth rate inhibition due to LAB growth is 95%, the increase of listeriosis per 100 000 people is 67-fold in the overall population in comparison to the no-growth of LM baseline scenario; (3) Reducing the mean shelf-life of smoked fish from 14 to 7 days, results in an 80% reduction of listeriosis. NA Medium:
Relative lag time concept for LM and LAB		 FAO-WHO [14]
Smoked fish (salmon and trout) Growth (Logistic model without delay, growth cardinal model) (1) Reducing home storage time from a max of 30 to 7 days, reduces the annual cases in 15% for salmon and in 45% for trout;(2) If all domestic temperatures had a mean temperature of 4.5ºC, the mean annual cases is reduced in 65% for salmon and in 70% for trout; (3) Combi-ning the two measures above reduces the mean annual cases in 75% for salmon and in 87% for trout; (4) If at purchase, LM counts would not exceed 100 CFU/g (truncating the baseline N~(1.01, 0.71) for smoked salmon and N~(1.35,1.40) for smoked trout, the mean annual cases would decrease in 22% in salmon and in 99% in trout. NA Low Garrido et al. [15]
Cold smoked salmon Growth (Baranyi model with Jameson effect LM and background microflora, Extended GR models for LM and LAB as a function of temperature, pH, aw, un-dissociated lactic acid, undissociated diacetate, phenols, dissolved CO2 and nitrite) (1) At a mean initial LM counts of 4 CFU/g, reducing the time of consumption from 28 to 14 days reduces the risk of illness in 64%; (2) If mean time of consumption is 14 days, reducing the mean initial counts from 25 CFU/g to 4 CFU/g reduces the risk of illness in 67%. NA Medium:
stochastic fluctuations in the GR of LM are taken into account by using white noise and the Winner process.
 Gospavic et al. [16]
Vacuum-packed cold smoked salmon Growth (Baranyi model, growth square root model) (1) If initial LM counts at retail (1-1000) was truncated at >100 CFU/g, the risk of illness would reduce in 0.3/0.9 log (high-risk and low-risk populations); (2) Reducing the maximum consumer shopping time from 3 hours to 30 min reduces risk of illness in 0.8/0.8 log; (3) Reducing consumer storage days from 21-30 to 7-15 days reduces risk of illness in 0.5/0.6 log; (4) Fixing storage temperature from 3-10ºC to 4ºC reduces risk of illness in 1.0/1.1 log; (5) If LM counts were not higher than 2 log CFU/g and reducing the maximum shopping time to 30 min, reducing consumer storage days to 7-15 days and storage temperature to 4ºC, reduces risk of illness in 1.32/1.39 log. Output – annual risk of illness in the high-risk population: (1) LM counts at retail (r=0.97); (2) Temperature in consumer fridge (r=0.13); (3) Time in consumer fridge (r=0.06). Low:
Lag: Baranyi model with bacterial adaptation
 Dass [17]
Traditional processed fish NA NA NA Low Bomfeh [18]
Cold smoked and salt-cured fishery products Growth (Logistic growth model, growth cardinal parameter model as a function of temperature, salt content, pH and phenolic compounds) (1) If home storage temperature decreased from 7ºC to 3ºC, the median cases of listeriosis per 100 000 elderly population would decrease in 70%.; (2) If home storage temperature decreased from 7ºC to 3ºC, the median cases of listeriosis per 100 000 working-age population would decrease in 40%. NA High: Parameters, including r, were estimated from a Bayesian model consisting of three linked modules: a model for the occurrence data, a model for the consumption data and a predictive model for the total number of cases in the population. The current model takes into account the possi-bility of continuing consumption of the same (contamina-ted) package of CSS/SCS, rather than assuming independent consumption days. Pasonen et al. [19]
aw: water activity; LPD: lag phase duration; RLT: relative lag time; MPD: maximum population density; GR: maximum growth rate; EGRx: exponential growth rate at x ºC; LAB: lactic acid bacteria; LAC: lactic acid concentration; RR: risk reduction; r: Pearson’s correlation coefficient; NA: not addressed in the study.
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