A meta-analysis accounting for sources of variability to estimate heat resistance reference parameters of bacteria using hierarchical Bayesian modeling: Estimation of D at 121.1 °C and pH 7, zT and zpH of Geobacillus stearothermophilus

Ready-to-eat Meat Plant Characteristics Associated with Food Safety Deficiencies During Regulatory Compliance Audits, Ontario, Canada

Food safety deficiencies in ready-to-eat (RTE) meat processing plants can increase foodborne disease risks. The purpose of this study was to identify common deficiencies and factors related to improved food safety performance in RTE meat plants in Ontario. Routine food safety audit records for licensed provincial free-standing meat processing plants (FSMPs) and abattoirs that process RTE meats were obtained and analyzed in Ontario, Canada, from 2015 to 2019. A Bayesian regression analysis was conducted to examine the association between selected plant characteristics and two outcomes: overall audit rating (pass vs. conditional pass or fail) and individual audit item fail rate. The audit rating was examined in a logistic model, while the audit item fail rate was evaluated in a negative binomial model. The majority (87.7%, n = 800/912) of audits resulted in a pass rating (compared to conditional pass or fail). The mean number of employees per plant, among 200/204 plants with employee data available, was 11.6 (SD = 20.6, range = 1-200). For the logistic regression model, FSMPs were predicted to have a much higher probability of passing audits than abattoirs (32.0% on average, with a 95% credible interval [CI] of 13.8-52.8%). The number of plant employees, water source (municipal vs. private), and types of RTE meat products produced had little to no consistent association with this outcome. The negative binomial model predicted a -0.009 points lower fail rate, on average, for audit items among FSMPs than abattoirs (95% CI: -0.001, -0.018). Meat plants producing jerky had a higher audit item fail rate compared to those that did not produce such products. The other investigated variables had little to no association with this outcome. The results found in this study can support and guide future inspection, audit and outreach efforts to reduce foodborne illness risks associated with RTE meats.

The importance of what we cannot observe: Experimental limitations as a source of bias for meta-regression models in predictive microbiology

Meta-regression models have gained in popularity during the last years as a way to create more generic models for Microbial Risk Assessments that also include variability. However, as with most meta-analyses and empirical models, systematic biases in the data can result in inaccurate models. In this article, we define experimental bias as a type of selection bias due to the practical limitations of microbial inactivation experiments. Conditions with extremely high D-values (i.e. slow inactivation) need very long experimental runs to cause significant reductions. On the other hand, when the D-value is extremely low, not enough data points can be gathered before the microbial population is below the detection limit. Consequently, experimental designs favour conditions within a practical experimental range, introducing a selection bias in the D-values. We demonstrate the impact of experimental bias in meta-regression models using numerical simulations. Models fitted to data with experimental bias overestimated the z-value and underestimated variability. We propose a rapid heuristic method to identify experimental bias in datasets, and we propose truncated regression to mitigate its impact in meta-regression models. Both methods were validated using simulated data. Thereafter the procedures were tested by building a meta-regression model for actual data for the inactivation of Bacillus cereus spores. We concluded that the dataset included experimental bias, and that it would cause an overestimation of the microbial resistance at high temperatures (>120 °C) for classical meta-regression models. This effect was mitigated when the model was built using truncated regression. In conclusion, we demonstrate that experimental bias could potentially result in inaccurate models for predictive microbiology. Therefore, checking for experimental bias should be a routine step in meta-regression modelling, and be included in guidelines on data analysis for meta-regression.

Evaluation of Strain Variability in Inactivation of Campylobacter jejuni in Simulated Gastric Fluid by Using Hierarchical Bayesian Modeling

This study was conducted to quantitatively evaluate the variability of stress resistance in different strains of Campylobacter jejuni and the uncertainty of such strain variability. We developed Bayesian statistical models with multilevel analysis to quantify variability within a strain, variability between different strains, and the uncertainty associated with these estimates. Furthermore, we measured the inactivation of 11 strains of C. jejuni in simulated gastric fluid with low pH, using the Weibullian survival model. The model was first developed for separate pH conditions and then analyzed over a range of pH levels. We found that the model parameters developed under separate pH conditions exhibited a clear dependence of survival on pH. In addition, the uncertainty of the variability between different strains could be described as the joint distribution of the model parameters. The latter model, including pH dependency, accurately predicted the number of surviving cells in individual as well as multiple strains. In conclusion, variabilities and uncertainties in inactivation could be simultaneously evaluated and interpreted via a probabilistic approach based on Bayesian theory. Such hierarchical Bayesian models could be useful for understanding individual-strain variability in quantitative microbial risk assessment. IMPORTANCE Since microbial strains vary in their growth and inactivation patterns in food materials, it is important to accurately predict these patterns for quantitative microbial risk assessment. However, most previous studies in this area have used highly resistant strains, which could lead to inaccurate predictions. Moreover, variability, including measurement errors and variability within a strain and between different strains, can contribute to predicted individual-level outcomes. Therefore, a multilevel framework is required to resolve these levels of variability and estimate their uncertainties. We developed a Bayesian predictive model for the survival of Campylobacter jejuni under simulated gastric conditions taking into account the variabilities and uncertainties. We demonstrated a high correspondence between predictions from the model and empirical measurements. The modeling procedure proposed in this study recommends a novel framework for predicting pathogen behavior, which can help improve quantitative microbial risk assessment during food production and distribution.

Multilevel modelling as a tool to include variability and uncertainty in quantitative microbiology and risk assessment. Thermal inactivation of Listeria monocytogenes as proof of concept

Variability is inherent in biology and also substantial for microbial populations. In the context of food safety risk assessment, it refers to differences in the response of different bacterial strains (between-strain variability) and different cells (within-strain variability) to the same condition (e.g. inactivation treatment). However, its quantification based on empirical observations and its incorporation in predictive models is a challenge for both experimental design and (statistical) analysis. In this article we propose the use of multilevel models to quantify (different levels of) variability and uncertainty and include them in the predictions. As proof of concept, we analyse the microbial inactivation of Listeria monocytogenes to thermal treatments including different levels of variability (between-strain and within-strain) and uncertainty. The relationship between the microbial count and time was expressed using a (non-linear) Weibullian model. Moreover, we defined stochastic hypotheses to describe the different types of variation at the level of the kinetic parameters, as well as in the observations (microbial counts). The model parameters (kinetic parameters and variances) are estimated using Bayesian statistics. The multilevel approach was compared against an analogous, single-level model. The multilevel methodology shrinks extreme parameter estimates towards the mean according to uncertainty, thus mitigating overfitting. In addition, this approach enables to easily incorporate different levels of variation (between-strain and/or within-strain variability and/or uncertainty) in the predictions. On the other hand, multilevel (Bayesian) models are more complex to define, implement, analyse and communicate than single-level models. Nevertheless, their ability to incorporate different sources of variability in predictions make them very suitable for Quantitative Microbial Risk Assessment.

Inactivation of Listeria monocytogenes, Staphylococcus aureus, and Salmonella enterica under High Hydrostatic Pressure: A Quantitative Analysis of Existing Literature Data

High hydrostatic pressure processing (HPP) is a mild preservation technique, and its use for processing foods has been widely documented in the literature. However, very few quantitative synthesis studies have been conducted to gather and analyze bacterial inactivation data to identify the mechanisms of HPP-induced bacterial inactivation. The purpose of this study was to conduct a quantitative analysis of three-decimal reduction times (t_3δ) from a large set of existing studies to determine the main influencing factors of HPP-induced inactivation of three foodborne pathogens (Listeria monocytogenes, Staphylococcus aureus, and Salmonella enterica) in various foods. Inactivation kinetics data sets from 1995 to 2017 were selected, and t_3δ values were first estimated by using the nonlinear Weibull model. Bayesian inference was then used within a metaregression analysis to build and test several models and submodels. The best model (lowest error and most parsimonious) was a hierarchical mixed-effects model including pressure intensity, temperature, study, pH, species, and strain as explicative variables and significant factors. Values for t_3δ and Z_P associated with inactivation under HPP were estimated for each bacterial pathogen, with their associated variability. Interstudy variability explained most of the variability in t_3δ values. Strain variability was also important and exceeded interstudy variability for S. aureus, which prevented the development of an overall model for this pathogen. Meta-analysis is not often used in food microbiology but was a valuable quantitative tool for modeling inactivation of L. monocytogenes and Salmonella in response to HPP treatment. Results of this study could be useful for refining quantitative assessment of the effects of HPP on vegetative foodborne pathogens or for more precisely designing costly and labor-intensive experiments with foodborne pathogens.

Bacillus thermoamylovorans Spores with Very-High-Level Heat Resistance Germinate Poorly in Rich Medium despite the Presence of ger Clusters but Efficiently upon Exposure to Calcium-Dipicolinic Acid

High-level heat resistance of spores of Bacillus thermoamylovorans poses challenges to the food industry, as industrial sterilization processes may not inactivate such spores, resulting in food spoilage upon germination and outgrowth. In this study, the germination and heat resistance properties of spores of four food-spoiling isolates were determined. Flow cytometry counts of spores were much higher than their counts on rich medium (maximum, 5%). Microscopic analysis revealed inefficient nutrient-induced germination of spores of all four isolates despite the presence of most known germination-related genes, including two operons encoding nutrient germinant receptors (GRs), in their genomes. In contrast, exposure to nonnutrient germinant calcium-dipicolinic acid (Ca-DPA) resulted in efficient (50 to 98%) spore germination. All four strains harbored cwlJ and gerQ genes, which are known to be essential for Ca-DPA-induced germination in Bacillus subtilis. When determining spore survival upon heating, low viable counts can be due to spore inactivation and an inability to germinate. To dissect these two phenomena, the recoveries of spores upon heat treatment were determined on plates with and without preexposure to Ca-DPA. The high-level heat resistance of spores as observed in this study (D120°C, 1.9 ± 0.2 and 1.3 ± 0.1 min; z value, 12.2 ± 1.8°C) is in line with survival of sterilization processes in the food industry. The recovery of B. thermoamylovorans spores can be improved via nonnutrient germination, thereby avoiding gross underestimation of their levels in food ingredients.

Foliar interception of radionuclides in dry conditions: a meta-analysis using a Bayesian modeling approach

Uncertainty on the parameters that describe the transfer of radioactive materials into the (terrestrial) environment may be characterized thanks to datasets such as those compiled within International Atomic Energy Agency (IAEA) documents. Nevertheless, the information included in these documents is too poor to derive a relevant and informative uncertainty distribution regarding dry interception of radionuclides by the pasture grass and the leaves of vegetables. In this paper, 145 sets of dry interception measurements by the aboveground biomass of specific plants were collected from published scientific papers. A Bayesian meta-analysis was performed to derive the posterior probability distributions of the parameters that reflect their uncertainty given the collected data. Four competing models were compared in terms of both fitting performances and predictive abilities to reproduce plausible dry interception data. The asymptotic interception factor, applicable whatever the species and radionuclide to the highest aboveground biomass values (e.g. mature leafy vegetables), was estimated with the best model, to be 0.87 with a 95% credible interval (0.85, 0.89).

Beyond QMRA: Modelling microbial health risk as a complex system using Bayesian networks

BACKGROUND

Quantitative microbial risk assessment (QMRA) is the current method of choice for determining the risk to human health from exposure to microorganisms of concern. However, current approaches are often constrained by the availability of required data, and may not be able to incorporate the many varied factors that influence this risk. Systems models, based on Bayesian networks (BNs), are emerging as an effective complementary approach that overcomes these limitations.

OBJECTIVES

This article aims to provide a comparative evaluation of the capabilities and challenges of current QMRA methods and BN models, and a scoping review of recent published articles that adopt the latter for microbial risk assessment. Pros and cons of systems approaches in this context are distilled and discussed.

METHODS

A search of the peer-reviewed literature revealed 15 articles describing BNs used in the context of QMRAs for foodborne and waterborne pathogens. These studies were analysed in terms of their application, uses and benefits in QMRA.

DISCUSSION

The applications were notable in their diversity. BNs were used to make predictions, for scenario assessment, risk minimisation, to reduce uncertainty and to separate uncertainty and variability. Most studies focused on a segment of the exposure pathway, indicating the broad potential for the method in other QMRA steps. BNs offer a number of useful features to enhance QMRA, including transparency, and the ability to deal with poor quality data and support causal reasoning.

CONCLUSION

The method has significant untapped potential to describe the complex relationships between microbial environmental exposures and health.

Meta-analysis of D-values of proteolytic Clostridium botulinum and its surrogate strain Clostridium sporogenes PA 3679

Foodborne botulism is a serious disease resulting from ingestion of preformed Clostridium botulinum neurotoxin in foodstuff. Since the 19th century, the heat resistance of this spore forming bacteria has been extensively studied in order to guarantee the public health associated with low acidic, ambient stable products. The most largely used heat resistance parameters in thermal settings of such products are the D121.1°C values (time required to have a 10-fold decrease of the spore count, at 121.1°C) and the z-values (temperature increase to have a 10-fold decrease of D-values). To determine D121.1°C and z-values of proteolytic C. botulinum and its nontoxigenic surrogate strain C. sporogenes PA3679, a dataset of 911 D-values was collected from 38 scientific studies. Within a meta-analysis framework, a mixed-effect linear model was developed with the log D-value (min) as response and the heat treatment temperature as explicative variable. The studies (38), the C. botulinum strains (11), and the heat treatment media (liquid media and various food matrices, split into nine categories in total) were considered as co-variables having a random effect. The species (C. botulinum and C. sporogenes) and the pH (five categories) were considered as co-variables having a fixed effect. Overall, the model gave satisfactory results with a residual standard deviation of 0.22. The heat resistance of proteolytic C. botulinum was found significantly lower than the C. sporogenes PA 3679 one: the mean D-values at the reference temperature of 121.1°C, in liquid media and pH neutral, were estimated to 0.19 and 1.28min for C. botulinum and C. sporogenes, respectively. On the other hand, the mean z-values of the two species were similar: 11.3 and 11.1°C for C. botulinum and C. sporogenes, respectively. These results will be applied to thermal settings of low-acid ambient stable products.

Meta-analysis of Campylobacter spp. survival data within a temperature range of 0 to 42°C

In Europe, Campylobacter is the leading reported cause of bacterial foodborne infectious disease. Quantifying its ability to survive at chilled and ambient temperatures and identifying the factors involved in variation in its survival may contribute to the development of efficient risk management strategies. A data set of 307 inactivation curves collected from the literature and the ComBase database, combined with 388 experimental curves, was analyzed with a log-linear model to obtain 695 D-values (time for 1 log inactivation). An additional 146 D-values collected from the literature or ComBase were added to the data set, for a total of 841 D-values. Because data were collected from different studies, the experimental conditions were somewhat heterogeneous (e.g., type of media or strain used). The full data set was then split into 19 different study types on which a meta-analysis was performed to determine the effect of temperature (range 0 to 42°C), Campylobacter species (C. coli and C. jejuni), and media (liquid media or meat matrix) on the survival ability of Campylobacter. A mixed-effects model, in which the study type and bacterial species were considered as random effects and the media and temperature as fixed effects, was run using a Bayesian approach. Overall, the model gave satisfactory results, with a residual standard deviation of 0.345 (the model response was the log D-value, expressed in days). In addition, the survival of Campylobacter was greater at 0 than at 42°C, with a log-linear pattern; the z-value (temperature to have a 10-fold decrease of D-value) was estimated to be 26.4°C (95 % interval: 23.9 to 29.4°C). Despite a significant media-species interaction term, it was established that both species were more resistant on the meat matrix than in liquid media. These results may be used to understand how Campylobacter can survive along the food chain, particularly in chilled environments, and consequently be transferred to other foodstuffs.