A statistical modelling approach for source attribution meta-analysis of sporadic infection with foodborne pathogens.

Numerous source attribution studies for foodborne pathogens based on epidemiological and microbiological methods are available. These studies provide empirical data for modelling frameworks that synthetize the quantitative evidence at our disposal and reduce reliance on expert elicitations. Here, we develop a statistical model within a Bayesian estimation framework to integrate attribution estimates from expert elicitations with estimates from microbial subtyping and case-control studies for sporadic infections with four major bacterial zoonotic pathogens in the Netherlands (Campylobacter, Salmonella, Shiga toxin-producing E. coli [STEC] O157 and Listeria). For each pathogen, we pooled the published fractions of human cases attributable to each animal reservoir from the microbial subtyping studies, accounting for the uncertainty arising from the different typing methods, attribution models, and year(s) of data collection. We then combined the population attributable fractions (PAFs) from the case-control studies according to five transmission pathways (domestic food, environment, direct animal contact, human-human transmission and travel) and 11 groups within the foodborne pathway (beef/lamb, pork, poultry meat, eggs, dairy, fish/shellfish, fruit/vegetables, beverages, grains, composite foods and food handlers/vermin). The attribution estimates were biologically plausible, allowing the human cases to be attributed in several ways according to reservoirs, transmission pathways and food groups. All pathogens were predominantly foodborne, with Campylobacter being mostly attributable to the chicken reservoir, Salmonella to pigs (albeit closely followed by layers), and Listeria and STEC O157 to cattle. Food-wise, the attributions reflected those at the reservoir level in terms of ranking. We provided a modelling solution to reach consensus attribution estimates reflecting the empirical evidence in the literature that is particularly useful for policy-making and is extensible to other pathogens and domains.

Comparative Exposure Assessment of ESBL-Producing Escherichia coli through Meat Consumption.

The presence of extended-spectrum ß-lactamase (ESBL) and plasmidic AmpC (pAmpC) producing Escherichia coli (EEC) in food animals, especially broilers, has become a major public health concern. The aim of the present study was to quantify the EEC exposure of humans in The Netherlands through the consumption of meat from different food animals. Calculations were done with a simplified Quantitative Microbiological Risk Assessment (QMRA) model. The model took the effect of pre-retail processing, storage at the consumers home and preparation in the kitchen (cross-contamination and heating) on EEC numbers on/in the raw meat products into account. The contribution of beef products (78%) to the total EEC exposure of the Dutch population through the consumption of meat was much higher than for chicken (18%), pork (4.5%), veal (0.1%) and lamb (0%). After slaughter, chicken meat accounted for 97% of total EEC load on meat, but chicken meat experienced a relatively large effect of heating during food preparation. Exposure via consumption of filet americain (a minced beef product consumed raw) was predicted to be highest (61% of total EEC exposure), followed by chicken fillet (13%). It was estimated that only 18% of EEC exposure occurred via cross-contamination during preparation in the kitchen, which was the only route by which EEC survived for surface-contaminated products. Sensitivity analysis showed that model output is not sensitive for most parameters. However, EEC concentration on meat other than chicken meat was an important data gap. In conclusion, the model assessed that consumption of beef products led to a higher exposure to EEC than chicken products, although the prevalence of EEC on raw chicken meat was much higher than on beef. The (relative) risk of this exposure for public health is yet unknown given the lack of a modelling framework and of exposure studies for other potential transmission routes.

Food Consumption and Handling Survey for Quantitative Microbiological Consumer Phase Risk Assessments.

In the consumer phase of a typical quantitative microbiological risk assessment (QMRA), mathematical equations identify data gaps. To acquire useful data we designed a food consumption and food handling survey (2,226 respondents) for QMRA applications that is especially aimed at obtaining quantitative data. For a broad spectrum of food products, the survey covered the following topics: processing status at retail, consumer storage, preparation, and consumption. Questions were designed to facilitate distribution fitting. In the statistical analysis, special attention was given to the selection of the most adequate distribution to describe the data. Bootstrap procedures were used to describe uncertainty. The final result was a coherent quantitative consumer phase food survey and parameter estimates for food handling and consumption practices in The Netherlands, including variation over individuals and uncertainty estimates.

Evaluation of exposure scenarios on intentional microbiological contamination in a drinking water distribution network.

Drinking water distribution networks are vulnerable to accidental or intentional contamination events. The objective of this study was to investigate the effects of seeding duration and concentration, exposure pathway (ingestion via drinking of water and tooth brushing and inhalation by taking a shower) and pathogen infectivity on exposure and infection risk in the case of an intentional pathogenic contamination in a drinking water distribution network. Seeding of a pathogen for 10 min and 120 min, and subsequent spreading through a drinking water distribution network were simulated. For exposure via drinking, actual data on drinking events and volumes were used. Ingestion of a small volume of water by tooth brushing twice a day by every person in the network was assumed. Inhalation of contaminated aerosol droplets took place when taking a shower. Infection risks were estimated for pathogens with low (r = 0.0001) and high (r = 0.1) infectivity. In the served population (48 000 persons) and within 24 h, about 1400 persons were exposed to the pathogen by ingestion of water in the 10-min seeding scenario and about 3400 persons in the 120-min scenario. The numbers of exposed persons via tooth brushing were about the same as via drinking of water. Showering caused (inhalation) exposure in about 450 persons in the 10-min scenario and about 1500 in the 120-min scenario. Regardless of pathogen infectivity, if the seeding concentration is 10(6) pathogens per litre or more, infection risks are close to one. Exposure by taking a shower is of relevance if the pathogen is highly infectious via inhalation. A longer duration of the seeding of a pathogen increases the probability of exposure.

Public Health Relevance of Cross-Contamination in the Fresh-Cut Vegetable Industry.

Although quantitative studies have revealed that cross-contamination during the washing stage of fresh produce occurs, the importance of cross-contamination in terms of public health relevance has rarely been assessed. The direct distribution of initially contaminated leafy vegetables to a multitude of servings by cutting and mixing also has not been addressed. The goal of this study was to assess the attribution of both contamination pathways to disease risk. We constructed a transparent and exploratory mathematical model that simulates the dispersion of contamination from a load of leafy greens during industrial washing. The risk of disease was subsequently calculated using a Beta-Poisson dose-response relation. The results indicate that up to contamination loads of 10(6) CFU the direct contamination route is more important than the indirect route (i.e., cross-contamination) in terms of number of illnesses. We highlight that the relevance of cross-contamination decreases with more diffuse and uniform contamination, and we infer that prevention of contamination in the field is the most important risk management strategy and that disinfection of washing water can be an additional intervention to tackle potentially high (>10(6) CFU) point contamination levels.