Tracing the Origin of Food-borne Disease Outbreaks: A Network Model Approach.

BACKGROUND: Food-borne disease outbreaks constitute a large health burden on society. One of the challenges when investigating such outbreaks is to trace the origin of the outbreak. In this study, we consider a network model to determine the spatial origin of the contaminated food product that caused the outbreak. METHODS: The network model we use replaces the classic geographic distance of a network by an effective distance so that two nodes connected by a long-range link may be more strongly connected than their geographic distance would suggest. Furthermore, the effective distance transforms complex spatial patterns into regular topological patterns, creating a means for easier identification of the origin of the spreading phenomenon. Because detailed information on food distribution is generally not available, the model uses the gravity model from economics: the flow of goods from one node to another increases with population size and decreases with the geographical distance between them. RESULTS: This effective distance network approach has been shown to perform well in a large Escherichia coli O104:H4 outbreak in Germany in 2011. In this article, we apply the same method to various food-borne disease outbreaks in the Netherlands. We found the effective distance network approach to fail in certain scenarios. CONCLUSIONS: Great care should be taken as to whether the underlying network model correctly captures the spreading mechanism of the outbreak in terms of spatial scale and single or multiple source outbreak.

A generalized Bayesian nonlinear mixed-effects regression model for zero-inflated longitudinal count data in tuberculosis trials.

In this paper, we investigate Bayesian generalized nonlinear mixed-effects (NLME) regression models for zero-inflated longitudinal count data. The methodology is motivated by and applied to colony forming unit (CFU) counts in extended bactericidal activity tuberculosis (TB) trials. Furthermore, for model comparisons, we present a generalized method for calculating the marginal likelihoods required to determine Bayes factors. A simulation study shows that the proposed zero-inflated negative binomial regression model has good accuracy, precision, and credibility interval coverage. In contrast, conventional normal NLME regression models applied to log-transformed count data, which handle zero counts as left censored values, may yield credibility intervals that undercover the true bactericidal activity of anti-TB drugs. We therefore recommend that zero-inflated NLME regression models should be fitted to CFU count on the original scale, as an alternative to conventional normal NLME regression models on the logarithmic scale.

Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection.

Early identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results. In this paper, we develop a formal Bayesian variable selection method to account for misclassified responses and missing covariates, which are common complications in food-borne outbreak investigations. We illustrate the implementation and performance of our method on a Salmonella Thompson outbreak in the Netherlands in 2012. Our method is shown to perform better than the standard logistic regression approach with respect to earlier identification of contaminated food products. It also allows relatively easy implementation of otherwise complex methodological issues.

Outbreak of Salmonella Bovismorbificans associated with the consumption of uncooked ham products, the Netherlands, 2016 to 2017.

In January 2017, an increase in reported Salmonellaenterica serotype Bovismorbificans cases in the Netherlands was observed since October 2016. We implemented a case-control study to identify the source, including all cases after December 2016. Adjusted odds ratios were calculated using logistic regression analysis. We traced back the distribution chain of suspected food items and sampled them for microbiological analysis. Human and food isolates were sequenced using whole genome sequencing (WGS). From October 2016 to March 2017, 54 S. Bovismorbificans cases were identified. Sequencing indicated that all were infected with identical strains. Twenty-four cases and 37 controls participated in the study. Cases were more likely to have consumed ham products than controls (aOR = 13; 95% CI: 2.0-77) and to have shopped at a supermarket chain (aOR = 7; 95% CI: 1.3-38). Trace-back investigations led to a Belgian meat processor: one retail ham sample originating from this processor tested positive for S. Bovismorbificans and matched the outbreak strain by WGS. All ham products related to the same batch were removed from the market to prevent further cases. This investigation illustrates the importance of laboratory surveillance for all Salmonella serotypes and the usefulness of WGS in an outbreak investigation.

A probabilistic approach to assess antibiotic resistance development risks in environmental compartments and its application to an intensive aquaculture production scenario.

Estimating antibiotic pollution and antibiotic resistance development risks in environmental compartments is important to design management strategies that advance our stewardship of antibiotics. In this study we propose a modelling approach to estimate the risk of antibiotic resistance development in environmental compartments and demonstrate its application in aquaculture production systems. We modelled exposure concentrations for 12 antibiotics used in Vietnamese Pangasius catfish production using the ERA-AQUA model. Minimum selective concentration (MSC) distributions that characterize the selective pressure of antibiotics on bacterial communities were derived from the European Committee on Antimicrobial Susceptibility Testing (EUCAST) Minimum Inhibitory Concentration dataset. The antibiotic resistance development risk (RDR) for each antibiotic was calculated as the probability that the antibiotic exposure distribution exceeds the MSC distribution representing the bacterial community. RDRs in pond sediments were nearly 100% for all antibiotics. Median RDR values in pond water were high for the majority of the antibiotics, with rifampicin, levofloxacin and ampicillin having highest values. In the effluent mixing area, RDRs were low for most antibiotics, with the exception of amoxicillin, ampicillin and trimethoprim, which presented moderate risks, and rifampicin and levofloxacin, which presented high risks. The RDR provides an efficient means to benchmark multiple antibiotics and treatment regimes in the initial phase of a risk assessment with regards to their potential to develop resistance in different environmental compartments, and can be used to derive resistance threshold concentrations.

Combining exposure and effect modeling into an integrated probabilistic environmental risk assessment for nanoparticles.

There is a growing need for good environmental risk assessment of engineered nanoparticles (ENPs). Environmental risk assessment of ENPs has been hampered by lack of data and knowledge about ENPs, their environmental fate, and their toxicity. This leads to uncertainty in the risk assessment. To deal with uncertainty in the risk assessment effectively, probabilistic methods are advantageous. In the present study, the authors developed a method to model both the variability and the uncertainty in environmental risk assessment of ENPs. This method is based on the concentration ratio and the ratio of the exposure concentration to the critical effect concentration, both considered to be random. In this method, variability and uncertainty are modeled separately so as to allow the user to see which part of the total variation in the concentration ratio is attributable to uncertainty and which part is attributable to variability. The authors illustrate the use of the method with a simplified aquatic risk assessment of nano-titanium dioxide. The authors' method allows a more transparent risk assessment and can also direct further environmental and toxicological research to the areas in which it is most needed. Environ Toxicol Chem 2016;35:2958-2967. © 2016 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Parametric estimation of P(X > Y) for normal distributions in the context of probabilistic environmental risk assessment.

Estimating the risk, P(X > Y), in probabilistic environmental risk assessment of nanoparticles is a problem when confronted by potentially small risks and small sample sizes of the exposure concentration X and/or the effect concentration Y. This is illustrated in the motivating case study of aquatic risk assessment of nano-Ag. A non-parametric estimator based on data alone is not sufficient as it is limited by sample size. In this paper, we investigate the maximum gain possible when making strong parametric assumptions as opposed to making no parametric assumptions at all. We compare maximum likelihood and Bayesian estimators with the non-parametric estimator and study the influence of sample size and risk on the (interval) estimators via simulation. We found that the parametric estimators enable us to estimate and bound the risk for smaller sample sizes and small risks. Also, the Bayesian estimator outperforms the maximum likelihood estimators in terms of coverage and interval lengths and is, therefore, preferred in our motivating case study.

Integrated probabilistic risk assessment for nanoparticles: the case of nanosilica in food.

Insight into risks of nanotechnology and the use of nanoparticles is an essential condition for the social acceptance and safe use of nanotechnology. One of the problems with which the risk assessment of nanoparticles is faced is the lack of data, resulting in uncertainty in the risk assessment. We attempt to quantify some of this uncertainty by expanding a previous deterministic study on nanosilica (5-200 nm) in food into a fully integrated probabilistic risk assessment. We use the integrated probabilistic risk assessment method in which statistical distributions and bootstrap methods are used to quantify uncertainty and variability in the risk assessment. Due to the large amount of uncertainty present, this probabilistic method, which separates variability from uncertainty, contributed to a better understandable risk assessment. We found that quantifying the uncertainties did not increase the perceived risk relative to the outcome of the deterministic study. We pinpointed particular aspects of the hazard characterization that contributed most to the total uncertainty in the risk assessment, suggesting that further research would benefit most from obtaining more reliable data on those aspects.