Impact of microbial count distributions on human health risk estimates

Quantitative assessment and genomic profiling of Campylobacter dynamics in poultry processing: a case study in the United Arab Emirates integrated abattoir system

In the United Arab Emirates, no previous research has investigated the dynamics of the foodborne pathogen Campylobacter in broiler abattoir processing. This study conducted in one of the largest poultry producers in the UAE, following each key slaughter stage-defeathering, evisceration, and final chilling-five broiler carcasses were collected from 10 slaughter batches over a year. Additionally, one caecum was obtained from 15 chickens in each slaughter batch to evaluate the flock colonization. In total, 300 samples (150 carcasses and 150 caeca) were collected and enumerated for Campylobacter using standard methods. Campylobacter was pervasive in caecal samples from all slaughter batches, with 86% of carcasses post-defeathering and evisceration stages and 94% post-chilling tested positive for Campylobacter. Campylobacter coli predominates in 55.2% of positive samples, followed by Campylobacter jejuni in 21%, with both species co-existing in 23.8% of the samples. Campylobacter counts in caecal contents ranged from 6.7 to 8.5 log10 CFU/g, decreasing post-defeathering and evisceration to 3.5 log10 CFU/g of neck skin and further to 3.2 log10 CFU/g of neck skin post-evisceration. After chilling, 70% of carcasses exceeded 3 log10 CFU/g of neck skin. Whole-genome sequencing (WGS) of 48 isolates unveiled diverse sequence types and clusters, with isolates sharing the same clusters (less than 20 single nucleotide polymorphisms) between different farms, different flocks within the same farm, as well as in consecutive slaughter batches, indicating cross-contamination. Multiple antimicrobial resistance genes and mutations in gyrA T86I (conferring fluoroquinolone resistance) and an RNA mutation (23S r.2075; conferring macrolide resistance) were widespread, with variations between C. coli and C. jejuni. WGS results revealed that selected virulence genes (pglG, pseD, pseI, flaA, flaB, cdtA, and cdtC) were significantly present in C. jejuni compared to C. coli isolates. This study offers the first insights into Campylobacter dynamics in poultry processing in the UAE. This work provides a base for future research to explore additional contributors to Campylobacter contamination in primary production. In conclusion, effective Campylobacter management demands a comprehensive approach addressing potential contamination sources at every production and processing stage, guided by continued microbiological surveillance and genomic analysis to safeguard public health and food safety.

Assessing the effect on the public health risk of current and alternative border control of Salmonella Typhimurium and Enteritidis in imported frozen poultry meat in Jordan

The JFDA applies border control for Salmonella Typhimurium and Salmonella Enteritidis in frozen poultry products. A QMRA model was developed to evaluate the effectiveness of this system in controlling the risk for consumers. The model consists of three modules; consumer phase, risk estimation, and risk reduction. The model inputs were the occurrence of Salmonella in different types of imported poultry products, the LOD of the Rapid'Salmonella, the number of tested samples of each batch, and the criteria for rejection. The model outputs were public health impact as the Minimum Relative Residual Risk (MRRR) given the batches' refusal and the percentage of Batches that are Not-compliant with the Microbiological Criteria (BNMC) of rejection. To estimate the overall MRRR of the border control, the estimated country and product-specific MRRR were summarized and weighted by the total imports of each product from each country. The current border control based on one sample per batch gives an overall MRRR value of 27%. The alternative scenarios based on three and five samples per batch are 12% and 8%, respectively. Overall, the higher the prevalence and/or concentration of Salmonella in imported products, the more the likelihood that batches will be rejected. For products with up-to-date data of occurrence, the estimated BNMC was similar to the observed proportion of rejected batches. The lack of data on the Salmonella concentrations in poultry products from different countries is the major source of the uncertainties in the model. It reduces our opportunities to obtain valid estimates of the absolute risk.

Comparison of Statistical Methods for Identifying Risk Factors for Salmonella Contamination of Whole Chicken Carcasses

ABSTRACT

The complexity of the food system makes analyzing microbiological data from food studies challenging because many of the assumptions (e.g., linear relationship between independent and dependent variable and independence of observations) associated with common analytical approaches (e.g., analysis of variance) are violated. Repeated sampling within an establishment introduces longitudinal correlation that must be accounted for during analyses. In this study, statistical methods for clustered or correlated data were used to determine how correlation impacts conclusions and to compare how assumptions associated with statistical methods impact the appropriateness of these methods within the context of food safety. Risk factor analyses for Salmonella contamination of whole chicken carcasses were conducted as a case study with regulatory data collected by the U.S. Department of Agriculture Food Safety and Inspection Service between May 2015 and December 2019 from 203 regulated establishments. Three models, generalized estimating equation, random effects, and logistic, were fit to Salmonella presence or absence data with establishment demographics and inspection history included as potential covariates. Beta parameter estimates and their standard errors and odds ratios and their 95% confidence intervals were compared across models. Conclusions drawn from the three models differed with respect to geographic region, whether the chicken establishment also slaughters turkeys, and establishment noncompliance with 9 CFR §417.4 (hazard analysis critical control point system validation, verification, and reassessment) in the 84 days leading up to sample collection. The results of this study reveal the need to consider clustering and correlation when analyzing food microbiological data, provide context for selecting a statistical method, and suggest that generalized estimating equation and random effects models are preferrable over logistic regression when analyzing correlated food data. These results support a renewed focus on statistical methodology in food safety.

HIGHLIGHTS

BIKE: Dietary Exposure Model for Foodborne Microbiological and Chemical Hazards

BIKE is a Bayesian dietary exposure assessment model for microbiological and chemical hazards. A graphical user interface was developed for running the model and inspecting the results. It is based on connected Bayesian hierarchical models, utilizing OpenBUGS and R in tandem. According to occurrence and consumption data given as inputs, a specific BUGS code is automatically written for running the Bayesian model in the background. The user interface is based on shiny app. Chronic and acute exposures are estimated for chemical and microbiological hazards, respectively. Uncertainty and variability in exposures are visualized, and a few optional model structures can be used. Simulated synthetic data are provided with BIKE for an example, resembling real occurrence and consumption data. BIKE is open source and available from github.

Human campylobacteriosis related to cross-contamination during handling of raw chicken meat: Application of quantitative risk assessment to guide intervention scenarios analysis in the Australian context

Quantitative Microbiological Risk Assessment (QMRA) is a methodology used to organize and analyze scientific information to both estimate the probability and severity of an adverse event as well as prioritize efforts to reduce the risk of foodborne pathogens. No QMRA efforts have been applied to Campylobacter in the Australian chicken meat sector. Hence, we present a QMRA model of human campylobacteriosis related to the occurrence of cross-contamination while handling raw chicken meat in Western Australia (WA). This work fills a gap in Campylobacter risk characterization in Australia and enables benchmarking against risk assessments undertaken in other countries. The model predicted the average probability of the occurrence of illness per serving of salad that became cross-contaminated from being handled following the handling of fresh chicken meat as 7.0 × 10^-4 (90% Confidence Interval [CI] ± 4.7 × 10^-5). The risk assessment model was utilized to estimate the likely impact of intervention scenarios on the predicted probability of illness (campylobacteriosis) per serving. Predicted relative risk reductions following changes in the retail prevalence of Campylobacter were proportional to the percentage desired in the reduction scenario; a target that is aiming to reduce the current baseline prevalence of Campylobacter in retail chicken by 30% is predicted to yield approximately 30% relative risk reduction. A simulated one-log reduction in the mean concentration of Campylobacter is anticipated to generate approximately 20% relative risk reductions. Relative risk reduction induced by a one-log decrease in the mean was equally achieved when the tail of the input distribution was affected-that is, by a change (one-log reduction) in the standard deviation of the baseline Campylobacter concentration. A scenario assuming a 5% point decrease in baseline probability of cross-contamination at the consumer phase would yield relative risk reductions of 14%, which is as effective as the impact of a strategic target of 10% reduction in the retail prevalence of Campylobacter. In conclusion, the present model simulates the probability of illness predicted for an average individual who consumes salad that has been cross-contaminated with Campylobacter from retail chicken meat in WA. Despite some uncertainties, this is the first attempt to utilize the QMRA approach as a scientific basis to guide risk managers toward implementing strategies to reduce the risk of human campylobacteriosis in an Australian context.

Antibiotic Resistances in Livestock: A Comparative Approach to Identify an Appropriate Regression Model for Count Data

Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i) to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model) and (ii) to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate model.

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring

A Bayesian statistical temporal-prevalence-concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch-specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within-batch and between-batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.

Detection of Coxiella burnetii in Ambient Air after a Large Q Fever Outbreak

One of the largest Q fever outbreaks ever occurred in the Netherlands from 2007-2010, with 25 fatalities among 4,026 notified cases. Airborne dispersion of Coxiella burnetii was suspected but not studied extensively at the time. We investigated temporal and spatial variation of Coxiella burnetii in ambient air at residential locations in the most affected area in the Netherlands (the South-East), in the year immediately following the outbreak. One-week average ambient particulate matter < 10 μm samples were collected at eight locations from March till September 2011. Presence of Coxiella burnetii DNA was determined by quantitative polymerase chain reaction. Associations with various spatial and temporal characteristics were analyzed by mixed logistic regression. Coxiella burnetii DNA was detected in 56 out of 202 samples (28%). Airborne Coxiella burnetii presence showed a clear seasonal pattern coinciding with goat kidding. The spatial variation was significantly associated with number of goats on the nearest goat farm weighted by the distance to the farm (OR per IQR: 1.89, CI: 1.31-2.76). We conclude that in the year after a large Q fever outbreak, temporal variation of airborne Coxiella burnetii is suggestive to be associated with goat kidding, and spatial variation with distance to and size of goat farms. Aerosol measurements show to have potential for source identification and attribution of an airborne pathogen, which may also be applicable in early stages of an outbreak.

Fitting a distribution to microbial counts: making sense of zeroes

The accurate estimation of true prevalence and concentration of microorganisms in foods is an important element of quantitative microbiological risk assessment (QMRA). This estimation is often based on microbial detection and enumeration data. Among such data are artificial zero counts, that originated by chance from contaminated food products. When these products are not differentiated from uncontaminated products that originate true zero counts, the estimates of true prevalence and concentration may be inaccurate. This inaccuracy is especially relevant in situations where highly pathogenic bacteria are involved and where growth can occur along the food pathway. Our aim was to develop a method that provides accurate estimates of concentration parameters and differentiates between artificial and true zeroes, thus also accurately estimating true prevalence. We first show the disadvantages of using a limit of quantification (LOQ) threshold for the analysis of microbial enumeration data. We show that, depending on the original distribution of concentrations and the LOQ value, it may be incorrect to treat artificial zeroes as censored below a quantification threshold. Next, a method is developed that estimates the true prevalence of contamination within a food lot and the parameters characterizing the within-lot distribution of concentrations, without assuming a LOQ, and using raw plate count data as an input. Counts resulting both from contaminated and uncontaminated sample units are analysed together. This procedure allows the estimation of the proportion of artificial zeroes among the total of zero counts, and therefore the estimation of true prevalence from enumeration results. We observe that this method yields best estimates of mean, standard deviation and prevalence at low true prevalence levels and low expected standard deviation. Furthermore, we conclude that the estimation of prevalence and the estimation of the distribution of concentrations are interrelated and therefore should be estimated simultaneously. We also conclude that one of the keys to an accurate characterization of the overall microbial contamination is the correct identification and separation of true and artificial zeroes. Our method for the analysis of quantitative microbial data shows a good performance in the estimation of true prevalence and the parameters of the distribution of concentrations, which indicates that it is a useful data analysis tool in the field of QMRA.