Use Of Quantitative Risk Assessment Research Articles

Prostate cancer risk stratification using magnetic resonance imaging-ultrasound fusion vs systematic prostate biopsy.

Image-guided approaches improve the diagnostic yield of prostate biopsy and frequently modify estimates of clinical risk. To better understand the impact of magnetic resonance imaging-ultrasound fusion targeted biopsy (MRF-TB) on risk assessment, we compared the distribution of National Comprehensive Cancer Network (NCCN) risk groupings, as calculated from MRF-TB vs systematic biopsy alone. We performed a retrospective analysis of 713 patients who underwent MRF-TB from January 2017 to July 2021. The primary study objective was to compare the distribution of National Comprehensive Cancer Network risk groupings obtained using MRF-TB (systematic + targeted) vs systematic biopsy. Systematic biopsy alone classified 10% of samples as very low risk and 18.7% of samples as low risk, while MRF-TB classified 10.5% of samples as very low risk and 16.1% of samples as low risk. Among patients with benign findings, low-risk disease, and favorable/intermediate-risk disease on systematic biopsy alone, 4.6% of biopsies were reclassified as high risk or very high risk on MRF-TB. Of 207 patients choosing active surveillance, 64 (31%), 91 (44%), 42 (20.2%), and 10 (4.8%) patients were classified as having very low-risk, low-risk, and favorable/intermediate-risk and unfavorable/intermediate-risk criteria, respectively. When using systematic biopsy alone, 204 patients (28.7%) were classified as having either very low-risk and low-risk disease per NCCN guidelines, while 190 men (26.6%) received this classification when using MRF-TB. The addition of MRF-TB to systematic biopsy may change eligibility for active surveillance in only a small proportion of patients with prostate cancer. Our findings support the need for routine use of quantitative risk assessment over risk groupings to promote more nuanced decision making for localized cancer.

Application of Defined Approaches to Assess Skin Sensitization Potency of Isothiazolinone Compounds.

Isothiazolinones (ITs) are widely used as antimicrobial preservatives in cosmetics and as additives for preservation of consumer and industrial products to control bacteria, fungi, and algae. Although they are effective biocides, they have the potential to produce skin irritation and sensitization, which poses a human health hazard. In this project, we evaluated nonanimal defined approaches (DAs) for skin sensitization that can provide point-of-departure estimates for use in quantitative risk assessment for ITs. The skin sensitization potential of six ITs was evaluated using three internationally harmonized nonanimal test methods: the direct peptide reactivity assay, KeratinoSens™, and the human cell line activation test. Results from these test methods were then applied to two versions of the Shiseido Artificial Neural Network DA. Sensitization hazard or potency predictions were compared with those of the in vivo murine local lymph node assay (LLNA). The nonanimal methods produced skin sensitization hazard and potency classifications concordant with those of the LLNA. EC3 values (the estimated concentration needed to produce a stimulation index of three, the threshold positive response) generated by the DAs had less variability than LLNA EC3 values, and confidence limits from the DAs overlapped those of the LLNA EC3 for most substances. The application of in silico models to in chemico and in vitro skin sensitization data is a promising data integration procedure for DAs to support hazard and potency classification and quantitative risk assessment.

Critical review and analysis of hydrogen safety data collection tools

The wider adoption of hydrogen in multiple sectors of the economy requires that safety and risk issues be rigorously investigated. Quantitative Risk Assessment (QRA) is an important tool for enabling safe deployment of hydrogen fueling stations and is increasingly embedded in the permitting process. QRA requires reliability data, and currently hydrogen QRA is limited by the lack of hydrogen specific reliability data, thereby hindering the development of necessary safety codes and standards [1]. Four tools have been identified that collect hydrogen system safety data: H2Tools Lessons Learned, Hydrogen Incidents and Accidents Database (HIAD), National Renewable Energy Lab's (NREL) Composite Data Products (CDPs), and the Center for Hydrogen Safety (CHS) Equipment and Component Failure Rate Data Submission Form. This work critically reviews and analyzes these tools for their quality and usability in QRA. It is determined that these tools lay a good foundation, however, the data collected by these tools needs improvement for use in QRA. Areas in which these tools can be improved are highlighted, and can be used to develop a path towards adequate reliability data collection for hydrogen systems.

Data requirements for improving the Quantitative Risk Assessment of liquid hydrogen storage systems

Quantitative Risk Assessment (QRA) supports the development of risk-informed safety codes and standards which are employed to enable the safe deployment of hydrogen technologies essential to decarbonize the transportation sector. System reliability data is a necessary input for rigorous QRA. The lack of reliability data for bulk liquid hydrogen (LH2) storage systems located on site at fueling stations limits the use of QRAs. In turn, this hinders the ability to develop the necessary safety codes and standards that enable worldwide deployment of these stations. Through a QRA-based analysis of a LH2 storage system, this work focuses on identifying relevant scenario and probability data currently available and ascertaining future data collection requirements regarding risks specific to liquid hydrogen releases. The work developed consists of the analysis of a general bulk LH2 storage system design located at a hydrogen fueling station. Failure Mode and Effect Analysis (FMEA) and traditional QRA modeling tools such as Event Sequence Diagrams (ESD) and Fault Tree Analysis (FTA) are employed to identify, rank, and model risk scenarios related to the release of LH2. Based on this analysis, scenario and reliability data needs to add LH2-related components to QRA are identified with the purpose of improving the future safety and risk assessment of these systems.

Utilizing literature-based rodent toxicology data to derive potency estimates for quantitative risk assessment

Evaluating the potential occupational health risk of engineered nanomaterials is an ongoing need. The objective of this meta-analysis, which consisted of 36 studies containing 86 materials, was to assess the availability of published in vivo rodent pulmonary toxicity data for a variety of nanoscale and microscale materials and to derive potency estimates via benchmark dose modeling. Additionally, the potency estimates based on particle mass lung dose associated with acute pulmonary inflammation were used to group materials based on toxicity. The commonalities among the physicochemical properties of the materials in each group were also explored. This exploration found that a material’s potency tended to be associated primarily with the material class based on chemical composition and form (e.g. carbon nanotubes, TiO2, ZnO) rather than with particular physicochemical properties. Limitations in the data available precluded a more extensive analysis of these associations. Issues such as data reporting and appropriate experimental design for use in quantitative risk assessment are the main reasons publications were excluded from these analyses and are discussed.

Weather-Based Predictive Modeling of Orange Rust of Sugarcane in Florida

Epidemics of sugarcane orange rust (caused by Puccinia kuehnii) in Florida are largely influenced by prevailing weather conditions. In this study, we attempted to model the relationship between weather conditions and rust epidemics as a first step toward development of a decision aid for disease management. For this purpose, rust severity data were collected from 2014 through 2016 at the Everglades Research and Education Center, Belle Glade, Florida, by recording percentage of rust-affected area of the top visible dewlap leaf every 2 weeks from three orange rust susceptible cultivars. Hourly weather data for 10- to 40-day periods prior to each orange rust assessment were evaluated as potential predictors of rust severity under field conditions. Correlation and stepwise regression analyses resulted in the identification of nighttime (8 PM to 8 AM) accumulation of hours with average temperature 20 to 22°C as a key predictor explaining orange rust severity. The five best regression models for a 30-day period prior to disease assessment explained 65.3 to 76.2% of variation of orange rust severity. Prediction accuracy of these models was tested using a case control approach with disease observations collected in 2017 and 2018. Based on receiver operator characteristic curve analysis of these two seasons of test data, a single-variable model with the nighttime temperature predictor mentioned above gave the highest prediction accuracy of disease severity. These models have potential for use in quantitative risk assessment of sugarcane rust epidemics.

How does dose impact on the severity of food-induced allergic reactions, and can this improve risk assessment for allergenic foods?: Report from an ILSI Europe Food Allergy Task Force Expert Group and Workshop.

Quantitative risk assessment (QRA) for food allergens has made considerable progress in recent years, yet acceptability of its outcomes remains stymied because of the limited extent to which it has been possible to incorporate severity as a variable. Reaction severity, particularly following accidental exposure, depends on multiple factors, related to the allergen, the host and any treatments, which might be administered. Some of these factors are plausibly still unknown. Quantitative risk assessment shows that limiting exposure through control of dose reduces the rates of reactions in allergic populations, but its impact on the relative frequency of severe reactions at different doses is unclear. Food challenge studies suggest that the relationship between dose of allergenic food and reaction severity is complex even under relatively controlled conditions. Because of these complexities, epidemiological studies provide very limited insight into this aspect of the dose‐response relationship. Emerging data from single‐dose challenges suggest that graded food challenges may overestimate the rate of severe reactions. It may be necessary to generate new data (such as those from single‐dose challenges) to reliably identify the effect of dose on severity for use in QRA. Success will reduce uncertainty in the susceptible population and improve consumer choice.

Application of in vitro skin penetration measurements to confirm and refine the quantitative skin sensitization risk assessment of methylisothiazolinone

Use of quantitative risk assessment (QRA) for assessing the skin sensitization potential of chemicals present in consumer products requires an understanding of hazard and product exposure. In the absence of data, consumer exposure is based on relevant habits and practices and assumes 100% skin uptake of the applied dose. To confirm and refine the exposure, a novel design for in vitro skin exposure measurements was conducted with the preservative, methylisothiazolinone (MI), in beauty care (BC) and household care (HHC) products using realistic consumer exposure conditions. A difference between measured exposure levels (MELs) for MI in leave-on versus rinse-off BC products, and lower MELs for MI in HHC rinse-off compared to BC products was demonstrated. For repeated product applications, the measured exposure was lower than estimations based on summation of applied amounts. Compared to rinse-off products, leave-on applications resulted in higher MELs, correlating with the higher incidences of allergic contact dermatitis associated with those product types. Lower MELs for MI in rinse-off products indicate a lower likelihood to induce skin sensitization, also after multiple daily applications. These in vitro skin exposure measurements indicate conservatism of default exposure estimates applied in skin sensitization QRA and might be helpful in future risk assessments.

Aggregating local, regional and global burden of disease impact assessment: detecting potential problem shifting in air quality policy making

Quantitative risk assessment (QRA) has been applied widely in environmental decision-making. Despite its advantage in taking locally specific details into consideration, problem shifting may occur due to its relatively narrow focus. To achieve a broader perspective, we propose that the combined use of QRA and life cycle assessment (LCA) may be beneficial. The feasibility and effectiveness of a hybrid approach of QRA and LCA were evaluated by considering alternative future air quality scenarios. Future air pollution emission scenarios based on different state-based environmental agency policies in Victoria, Australia, were considered to explore ways to reduce problem shifting. QRA was used to estimate human burden of disease (BoD) for the local population due to particulates and tropospheric ozone under ‘Low Impact’ and ‘Most Likely’ scenarios. To detect potential problem shifting, LCA was performed for other associated impact categories such as climate change potential. Disability-adjusted life years (DALYs) were used as a common metric to facilitate direct comparisons and aggregation between results of QRA and LCA. While the ‘Low Impact’ scenario was developed to project a scenario with reduced human health impacts (among other environmental impacts), the overall assessment revealed that BoD impacts may suffer a net increase under this scenario. The QRA suggested about 0.74 kilo DALYs (kDALYs) were saved annually; however, the LCA indicated that more impacts were expected, over 8 kDALYs per year. Hence, it was projected that reducing local impact by improving local air quality would shift more problems elsewhere. Therefore, using QRA alone that focuses on local impacts could result in an inappropriate scenario being preferred and improper policies being set. The outcomes of this study revealed the importance of applying a broad perspective in environmental decision-making. QRA appeared to be a suitable tool to assess local BoD impacts caused by ambient air pollutants. However, by also including LCA, impacts could be more comprehensively assessed. Combined use of QRA and LCA enables the analyst to account for locally specific considerations while reducing potential problem shifting to more distant receptors.

Historical Context and Recent Advances in Exposure-Response Estimation for Deriving Occupational Exposure Limits

Virtually no occupational exposure standards specify the level of risk for the prescribed exposure, and most occupational exposure limits are not based on quantitative risk assessment (QRA) at all. Wider use of QRA could improve understanding of occupational risks while increasing focus on identifying exposure concentrations conferring acceptably low levels of risk to workers. Exposure-response modeling between a defined hazard and the biological response of interest is necessary to provide a quantitative foundation for risk-based occupational exposure limits; and there has been considerable work devoted to establishing reliable methods quantifying the exposure-response relationship including methods of extrapolation below the observed responses. We review several exposure-response modeling methods available for QRA, and demonstrate their utility with simulated data sets.

Quantile benchmark dose estimation for continuous endpoints

Quantitative risk assessment (QRA) characterizes the risk of an adverse health outcome for an organism exposed to a chemical, environmental, physical, or other hazard. Historically, QRAs define risk based upon the increased probability of adverse response because of exposure when compared with the background probability of response in unexposed individuals. For a specified risk level, the exposure–response relationship is inverted to find an exposure (or dose) for minimizing risk; these exposures are often called the benchmark dose (BMD). For continuous endpoints, BMD analyses have employed strong assumptions on the form of the response distribution which may not be met for most toxicology data sets. We propose a reformulation of the BMD for use in QRA for continuous response that is based upon milder assumptions using quantile regression and monotone smoothing splines. This method takes into account the uncertainty in the response distribution as well as uncertainty in the exposure–response relationship. We apply this method to a data example and show through a simulation study that the approach is often superior to current practice. Copyright © 2015 John Wiley & Sons, Ltd.

Risk assessment and risk management for safe foods: Assessment needs inclusion of variability and uncertainty, management needs discrete decisions

The introduction of relevant food safety changes in legislation, like time–temperature criteria for pasteurisation and sterilisation, microbiological criteria, HACCP and FSOs, generally took several decades. All these approaches have helped to define specific targets or systems to improve the management of food safety. More and more the measures could be related to specific efficiency in public health protection. With the use of quantitative risk assessment, theoretically the effect of all interventions on the final risk can be determined, which can help to design the appropriate controls in the food safety management system. In such an assessment in practice, however results have understandably large variability and also uncertainty. There is large variability and uncertainty in the biological parts of the assessment, the dose response (infectivity, human susceptibility) the micro-organism kinetics in the chain (growth, inactivation, stress response) and also in the more technological parts, the conditions in the chain and the consumer behaviour. Often the results of risk assessments are probability distributions of the variability in illness probability, also sometimes represented with their uncertainty. To make a link from these distributions to managerial decisions, that need to be black and white, should not be considered the job of risk managers. This link needs investment from both the assessor and the manager.

Risk-Informed Interference Assessment: A Quantitative Basis for Spectrum Allocation Decisions

The trade-off between the benefits of a new radio service allocation and its risk to incumbents has to date been based on “worst case” analyses that focus on the single scenario with the most severe negative consequence, regardless of its likelihood. This is no longer a tenable approach since it leads to over-conservative allocations that limit the social benefits of new services while giving incumbents more protection than they need.This paper describes an alternative to worst case reasoning: the use of quantitative risk assessment (QRA) to analyze the harm that may be caused by changes in radio service rules. QRA considers both the likelihood and the consequences for multiple hazard scenarios, not just the most severe consequence, regardless of likelihood, as in worst case. While quantitative risk assessment has been used for decades in many regulated industries it has not yet been applied to spectrum management. This paper identifies four key elements of a systematic, quantitative analysis of radio interference hazards, and illustrates them with examples: (1) make an inventory of all significant harmful interference hazard modes; (2) define a consequence metric to characterize the severity of hazards; (3) assess the likelihood and consequence of each hazard mode; and (4) aggregate them into a basis for decision making. The four elements are illustrated by examples from spectrum policy questions past and present; the focal case study is the determination of geographic exclusion zones to protect weather satellite earth stations from co-channel cellular mobile transmitters.In order to reap the benefits of risk-informed interference analysis, the paper rec-ommends that regulators (1) develop know-how through a variety of educational initiatives; (2) use quantitative risk assessment in their analysis of harmful interference, and publish the results; and (3) pilot this approach in proceedings with limited scope, like selected site-specific license waiver proceedings. There is also a role for legislators and the executive branch: (1) they can require risk-informed assessments as part of their spectrum oversight activities; (2) when presented with politically charged claims of harmful interference, they can avoid the temptation of nightmare scenarios and instead make assessments that consider both the likelihood and consequences of interference harms; and (3) they can support regulators that use risk-informed interference assessments rather than basing decisions on worst case analysis.

The use of quantitative risk assessment to assess lifetime welfare outcomes for breech strike and mulesing management options in Merino sheep

AbstractIn Australia, flystrike can severely compromise sheep welfare. Traditionally, the surgical practice of mulesing was performed to alter wool distribution and breech conformation and thereby reduce flystrike risk. The aim of this study was to use published data to evaluate the effectiveness of an epidemiologically based risk assessment model in comparing welfare outcomes in sheep undergoing mulesing, mulesing with pain relief, plastic skin-fold clips, and no mulesing. We used four measures, based on cortisol, haptoglobin, bodyweight and behavioural change, across three farming regions in Australia. All data were normalised to a common scale, based on the range between the highest and lowest responses for each variable (‘welfare impact’; I). Lifetime severity of welfare challenge (SWC) was estimated by summing annual SWCs (SWC = I × P, where P = probability of that impact occurring). The severity of welfare challenge during the first year of life was higher for mulesed animals compared to unmulesed. However, over five years of life, the highest severity of welfare challenge was for unmulesed animals, and the lowest was for the plastic skin-fold clips. The model produced estimates of SWC that are in broad agreement with expert consensus that, although mulesing historically represented a welfare benefit for sheep under Australian conditions, the replacement of mulesing with less invasive procedures, and ultimately genetic selection combined with anti-fly treatments, will provide a sustainable welfare benefit. However, the primary objective of this work was to evaluate the use of the risk assessment framework; not to compare welfare outcomes from mulesing and its alternatives.

Quantitative risk assessment for the transport of ammonia by rail

AbstractRisk assessments are considered to be an essential tool in ensuring the safety of engineering projects. The benefits of using risks assessments have prompted its acceptance in the safety legislation for a number of industries. The use of quantitative risk assessment for rail transportation of hazardous materials has gained more attention in recent years, because the amount of the transported hazardous materials is large, and the transportation route often passes through populated areas, such as cities. In this study, the risk for the ammonia transportation by rail in Malaysia was conducted by combining the results from a previous failure frequency analysis and consequence analysis. The assessment results acknowledged the significant effects of train speeds on the overall individual risk. The risk with a tolerable risk limit of 1 × 10−6 per year increased significantly with more train accidents occurring at increasing speeds. Most of the surrounding populations along the transportation route analyzed are exposed to higher risk levels than the tolerable limit. This risk exposure to the public demonstrates the need for proper planning of moving ammonia through populated areas. © 2009 American Institute of Chemical Engineers Process Saf Prog, 2010

The Use of QRA To Inform the Design of a High-Pressure Onshore Pipeline

Summary The management of onshore-pipeline safety in the United Kingdom is governed by the Pipelines Safety Regulations, 1996. This requires pipeline operators to design, build, and operate pipelines to ensure that they are safe, so far as is reasonably practicable. This is achieved, in part, by applying good practice and designing the pipeline to recognized onshore-pipeline design codes. Where the proposed design falls outside code guidance, a quantitative risk assessment (QRA) provides an effective means of demonstrating a safe design. This paper describes a study that was carried out to define the design requirements for the onshore section of a multiphase pipeline. The proposed pipeline will have a design pressure of 380 barg. However, onshore design codes PD 8010-1 (2004) and IGE/TD/1 (Steel Pipelines 2001) do not provide guidance on the design of pipelines with pressures exceeding 100 barg. Experience from other projects indicates that it is important to consider the implications of such high design pressures early in a project. The QRA considered a number of different design options. The safety risks of the proposed pipeline options were compared with those from a typical 100-barg-gas-transmission pipeline of the same diameter. This means that the risk of the proposed pipeline can be compared in a relative manner, to an acceptable design, and in an absolute manner to limits on individual and societal risk. The results of QRAs of the different design options are summarized and discussed. The effects of mitigation measures, such as reducing the pipeline-design factor (the ratio of the hoop stress to the specified minimum yield strength), increasing the wall thickness, and incorporating a pressure-limiting system at the landfall, are illustrated. It is shown that the proposed design is feasible. The individual and societal risks for the proposed design were lower than that of a typical 100-barg-gas-transmission pipeline. The individual risk was in the broadly accepted region of risk, as defined by the UK Health and Safety Executive (HSE), and the societal risk was below the acceptance criterion given in IGE/TD/1 (Steel Pipelines 2001).

Risk-based standards for Campylobacter in the broiler meat chain

To control the risks of Campylobacter in broiler meat, microbiological criteria in the food chain are proposed for testing and scheduling, that is testing the level of contamination in a flock and diverting highly contaminated flocks away from fresh meat production. By applying quantitative risk assessment, the effects of this strategy for different test sensitivities at different stages in the food chain are evaluated for their expected impact on consumer health risk and the percentage of flocks to be scheduled. This offers a practical tool to compare costs and benefits of different risk management options and demonstrates the use of quantitative risk assessment to set risk-based standards.

Quantification of risks to consumers’ health and to company’s incomes due to failures in food safety

Quality assurance systems are required at each step in the food production chain to ensure safety of food and to show compliance with regulatory and customer requirements. This is a matter affecting food companies too as part of the business activity and a challenge since they are the main beneficiary, i.e. economically, when it offers a safe product and the worst affected otherwise. Thus, failures in food safety endanger not only consumers’ health but also put at risk the company’s profitability. This paper introduces the fundamentals and an example of application of the use of quantitative risk assessment (QRA) to estimate the risk to consumers’ health and the induced company’s economic losses, which can be of great support for the risk-informed decision-making in the food processing context. The information provided by the QRA could be used in this food industry context to prioritize the safety management measures needed according to real importance of the main hazards identified for a particular food processing, which simultaneously would better protect the consumer’s health and be cost effective and efficient for the food industry. The example of application of this QRA on Listeria monocytogenes in raw fish shows that the risk for consumers’ health imposed by the product of this company ranges normal values as compared with the survey in FDA/USDA and the risk for the company due to induced economic losses stays low in average, although an upper bound for such losses over a year period has been found significant due to both direct and indirect costs.

Probabilistic Weight Distribution of French Population and its Use in Quantitative Risk Assessment

P-784 Introduction: Exposure assessment is an important step in risk assessment. Exposure is assessed using plausible scenarios taking into account the anthropometric and physiological characteristics of the population as well as their local behavior. These characteristics are commonly known as human exposure variables. As an example, body weight (BW), water intake… BW is often used in risk assessment to estimate the exposure of the population to a given pollutant. In a deterministic risk assessment, a single point value is used (for example, each adult is supposed to weigh 70 kilograms). Since everyone has not the same weight, in the probabilistic risk assessment (PRA), BW is considered as a random variable instead of a point value. The best way to take into account the variability of BW in population is to model it by a full probability distribution. Defining probability distributions of BW by age/gender categories in the French population is the objective of this work. Methods: To reach this objective, a sufficient amount of raw data that accurately represent the population of interest is needed. To this purpose, we used the INSEE (French National Institute for Statistics and Economic Studies) health study conducted from 2002 to 2003. The sample size used in this work was 35000 persons. The choice of the theoretical distribution which best models the observed distribution of BW is based initially on statistical tests and graphical exploration. The final choice is refined by comparing descriptive statistics of the theoretical and observed distributions. Results: The three-parameter lognormal distribution is the best model that fits the observed BW data for each age/gender category. As an example, figure 1 shows BW distribution for men aged 30–34 years.FIGURE 1: Body weight (kg) for man aged 30–34 years.Discussion and Conclusion: This study allows constructing the probability distributions of BW with the objective of using it in PRA. It has the advantage of being based on data which are representative of the French population. Until now, only the American references were available. However BW data are declared and not measured, with the end result of slightly underestimating the fitted distributions. Much attention has been devoted in the last few years to the methods and the interest of conducting PRA. Applying this approach to other human exposure variables using French data is now necessary.

Effect of temperature, water-phase salt and phenolic contents on Listeria monocytogenes growth rates on cold-smoked salmon and evaluation of secondary models

Salting and smoking are ancient processes for fish preservation. The effects of salt and phenolic smoke compounds on the growth rate of L. monocytogenes in cold-smoked salmon were investigated through physico-chemical analyses, challenge tests on surface of cold-smoked salmon at 4 °C and 8 °C, and a survey of the literature. Estimated growth rates were compared to predictions of existing secondary models, taking into account the effects of temperature, water phase salt content, phenolic content, and additional factors (e.g. pH, lactate, dissolved CO 2). The secondary model proposed by Devlieghere et al. [Devlieghere, F., Geeraerd, A.H., Versyck, K.J., Vandewaetere, B., van Impe, J., Debevere, J., 2001. Growth of Listeria monocytogenes in modified atmosphere packed cooked meat products: a predictive model. Food Microbiology 18, 53–66.] and modified by Giménez and Dalgaard [Giménez, B., Dalgaard, P., 2004. Modelling and predicting the simultaneous growth of Listeria monocytogenes and spoilage micro-organisms in cold-smoked salmon. Journal of Applied Microbiology 96, 96–109.] appears appropriate. However, further research is needed to understand all effects affecting growth of L. monocytogenes in cold-smoked salmon and to obtain fully validated predictive models for use in quantitative risk assessment.