Arno Swart

Propidium monoazide does not fully inhibit the detection of dead Campylobacter on broiler chicken carcasses by qPCR

A real time quantitative PCR combined with propidium monoazide (PMA) treatment of samples was implemented to quantify live C. jejuni, C. coli and C. lari on broiler chicken carcasses at selected processing steps in the slaughterhouse. The samples were enumerated by culture for comparison. The Campylobacter counts determined with the PMA-qPCR and the culture method were not concordant. We conclude that the qPCR combined with PMA treatment of the samples did not fully reduce the signal from dead cells.

Impact of Acquired Immunity and Dose-Dependent Probability of Illness on Quantitative Microbial Risk Assessment

Dose-response models in microbial risk assessment consider two steps in the process ultimately leading to illness: from exposure to (asymptomatic) infection, and from infection to (symptomatic) illness. Most data and theoretical approaches are available for the exposure-infection step; the infection-illness step has received less attention. Furthermore, current microbial risk assessment models do not account for acquired immunity. These limitations may lead to biased risk estimates. We consider effects of both dose dependency of the conditional probability of illness given infection, and acquired immunity to risk estimates, and demonstrate their effects in a case study on exposure to Campylobacter jejuni. To account for acquired immunity in risk estimates, an inflation factor is proposed. The inflation factor depends on the relative rates of loss of protection over exposure. The conditional probability of illness given infection is based on a previously published model, accounting for the within-host dynamics of illness. We find that at low (average) doses, the infection-illness model has the greatest impact on risk estimates, whereas at higher (average) doses and/or increased exposure frequencies, the acquired immunity model has the greatest impact. The proposed models are strongly nonlinear, and reducing exposure is not expected to lead to a proportional decrease in risk and, under certain conditions, may even lead to an increase in risk. The impact of different dose-response models on risk estimates is particularly pronounced when introducing heterogeneity in the population exposure distribution.

Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock – A review to inform risk assessment studies

n Abstractn n In this review we discuss studies that applied atmospheric dispersion models (ADM) to bioaerosols that are pathogenic to humans and livestock in the context of risk assessment studies. Traditionally, ADMs have been developed to describe the atmospheric transport of chemical pollutants, radioactive matter, dust, and particulate matter. However, they have also enabled researchers to simulate bioaerosol dispersion.n To inform risk assessment, the aims of this review were fourfold, namely (1) to describe the most important physical processes related to ADMs and pathogen transport, (2) to discuss studies that focused on the application of ADMs to pathogenic bioaerosols, (3) to discuss emission and inactivation rate parameterisations, and (4) to discuss methods for conversion of concentrations to infection probabilities (concerning quantitative microbial risk assessment).n The studies included human, livestock, and industrial sources. Important factors for dispersion included wind speed, atmospheric stability, topographic effects, and deposition. Inactivation was mainly governed by humidity, temperature, and ultraviolet radiation.n A majority of the reviewed studies, however, lacked quantitative analyses and application of full quantitative microbial risk assessments (QMRA). Qualitative conclusions based on geographical dispersion maps and threshold doses were encountered frequently. Thus, to improve risk assessment for future outbreaks and releases, we recommended determining well-quantified emission and inactivation rates and applying dosimetry and dose–response models to estimate infection probabilities in the population at risk.n n

Improved correlation of human Q fever incidence to modelled C. burnetii concentrations by means of an atmospheric dispersion model

BackgroundAtmospheric dispersion models (ADMs) may help to assess human exposure to airborne pathogens. However, there is as yet limited quantified evidence that modelled concentrations are indeed associated to observed human incidence.MethodsWe correlated human Q fever (caused by the bacterium Coxiella burnetii) incidence data in the Netherlands to modelled concentrations from three spatial exposure models: 1) a NULL model with a uniform concentration distribution, 2) a DISTANCE model with concentrations proportional to the distance between the source and residential addresses of patients, and 3) concentrations modelled by an ADM using three simple emission profiles. We used a generalized linear model to correlate the observed incidences to modelled concentrations and validated it using cross-validation.ResultsADM concentrations generally correlated the best to the incidence data. The DISTANCE model always performed significantly better than the NULL model. ADM concentrations based on wind speeds exceeding threshold values of 0 and 2 m/s performed better than those based on 4 or 6 m/s. This might indicate additional exposure to bacteria originating from a contaminated environment.ConclusionsBy adding meteorological information the correlation between modelled concentration and observed incidence improved, despite using three simple emission profiles. Although additional information is needed – especially regarding emission data - these results provide a basis for the use of ADMs to predict and to visualize the spread of airborne pathogens during livestock, industry and even bio-terroristic related outbreaks or releases to a surrounding human population.

Reduction of extended-spectrum-β-lactamase- and AmpC-β-lactamase-producing Escherichia coli through processing in two broiler chicken slaughterhouses

Whilst broilers are recognised as a reservoir of extended-spectrum-β-lactamase (ESBL)- and AmpC-β-lactamase (AmpC)-producing Escherichia coli, there is currently limited knowledge on the effect of slaughtering on its concentrations on poultry meat. The aim of this study was to establish the concentration of ESBL/AmpC producing E. coli on broiler chicken carcasses through processing. In addition the changes in ESBL/AmpC producing E. coli concentrations were compared with generic E. coli and Campylobacter. In two slaughterhouses, the surface of the whole carcasses was sampled after 5 processing steps: bleeding, scalding, defeathering, evisceration and chilling. In total, 17 batches were sampled in two different slaughterhouses during the summers of 2012 and 2013. ESBL/AmpC producing E. coli was enumerated on MacConkey agar with 1mg/l cefotaxime, and the ESBL/AmpC phenotypes and genotypes were characterised. The ESBL/AmpC producing E. coli concentrations varied significantly between the incoming batches in both slaughterhouses. The concentrations on broiler chicken carcasses were significantly reduced during processing. In Slaughterhouse 1, all subsequent processing steps reduced the concentrations except evisceration which led to a slight increase that was statistically not significant. The changes in concentration between processing steps were relatively similar for all sampled batches in this slaughterhouse. In contrast, changes varied between batches in Slaughterhouse 2, and the overall reduction through processing was higher in Slaughterhouse 2. Changes in ESBL/AmpC producing E. coli along the processing line were similar to changes in generic E. coli in both slaughterhouses. The effect of defeathering differed between ESBL/AmpC producing E. coli and Campylobacter. ESBL/AmpC producing E. coli decreased after defeathering, whereas Campylobacter concentrations increased. The genotypes of ESBL/AmpC producing E. coli (blaCTX-M-1, blaSHV-12, blaCMY-2, blaTEM-52c, blaTEM-52cvar) from both slaughterhouses match typical poultry genotypes. Their distribution differed between batches and changed throughout processing for some batches. The concentration levels found after chilling were between 10(2) and 10(5)CFU/carcass. To conclude, changes in ESBL/AmpC producing E. coli concentrations on broiler chicken carcasses during processing are influenced by batch and slaughterhouse, pointing to the role of both primary production and process control for reducing ESBL/AmpC producing E. coli levels in final products. Due to similar changes upon processing, E. coli can be used as a process indicator of ESBL/AmpC producing E. coli, because the processing steps had similar impact on both organisms. Cross contamination may potentially explain shifts in genotypes within some batches through the processing.

A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment

Background Source identification in areas with outbreaks of airborne pathogens is often time-consuming and expensive. We developed a model to identify the most likely location of sources of airborne pathogens. Methods As a case study, we retrospectively analyzed three Q fever outbreaks in the Netherlands in 2009, each with suspected exposure from a single large dairy goat farm. Model input consisted only of case residential addresses, day of first clinical symptoms, and human population density data. We defined a spatial grid and fitted an exponentially declining function to the incidence-distance data of each grid point. For any grid point with a fit significant at the 95% confidence level, we calculated a measure of risk. For validation, we used results from abortion notifications, voluntary (2008) and mandatory (2009) bulk tank milk sampling at large (i.e. >50 goats and/or sheep) dairy farms, and non-systematic vaginal swab sampling at large and small dairy and non-dairy goat/sheep farms. In addition, we performed a two-source simulation study. Results Hotspots – areas most likely to contain the actual source – were identified at early outbreak stages, based on the earliest 2–10% of the case notifications. Distances between the hotspots and suspected goat farms varied from 300–1500 m. In regional likelihood rankings including all large dairy farms, the suspected goat farms consistently ranked first. The two-source simulation study showed that detection of sources is most clear if the distance between the sources is either relatively small or relatively large. Conclusions Our model identifies the most likely location of sources in an airborne pathogen outbreak area, even at early stages. It can help to reduce the number of potential sources to be investigated by microbial testing and to allow rapid implementation of interventions to limit the number of human infections and to reduce the risk of source-to-source transmission.

Human Q fever incidence is associated to spatiotemporal environmental conditions

Airborne pathogenic transmission from sources to humans is characterised by atmospheric dispersion and influence of environmental conditions on deposition and reaerosolisation. We applied a One Health approach using human, veterinary and environmental data regarding the 2009 epidemic in The Netherlands, and investigated whether observed human Q fever incidence rates were correlated to environmental risk factors. We identified 158 putative sources (dairy goat and sheep farms) and included 2339 human cases. We performed a high-resolution (1 × 1 km) zero-inflated regression analysis to predict incidence rates by Coxiella burnetii concentration (using an atmospheric dispersion model and meteorological data), and environmental factors – including vegetation density, soil moisture, soil erosion sensitivity, and land use data – at a yearly and monthly time-resolution. With respect to the annual data, airborne concentration was the most important predictor variable (positively correlated to incidence rate), followed by vegetation density (negatively). The other variables were also important, but to a less extent. High erosion sensitive soils and the land-use fractions “city” and “forest” were positively correlated. Soil moisture and land-use “open nature” were negatively associated. The geographical prediction map identified the largest Q fever outbreak areas. The hazard map identified highest hazards in a livestock dense area. We conclude that environmental conditions are correlated to human Q fever incidence rate. Similar research with data from other outbreaks would be needed to more firmly establish our findings. This could lead to better estimations of the public health risk of a C. burnetii outbreak, and to more detailed and accurate hazard maps that could be used for spatial planning of livestock operations.

Climate change effects on airborne pathogenic bioaerosol concentrations: a scenario analysis

The most recent IPCC report presented further scientific evidence for global climate change in the twenty-first century. Important secondary effects of climate change include those on water resource availability, agricultural yields, urban healthy living, biodiversity, ecosystems, food security, and public health. The aim of this explorative study was to determine the range of expected airborne pathogen concentrations during a single outbreak or release in a future climate compared to a historical climatic period (1981–2010). We used five climate scenarios for the periods 2016–2045 and 2036–2065 defined by the Royal Netherlands Meteorological Institute and two conversion tools to create hourly future meteorological data sets. We modelled season-averaged airborne pathogen concentrations by means of an atmospheric dispersion model and compared these data to historical (1981–2010) modelled concentrations. Our results showed that modelled concentrations were modified several percentage points on average as a result of climate change. On average, concentrations were reduced in four out of five scenarios. Wind speed and global radiation were of critical importance, which determine horizontal and vertical dilution. Modelled concentrations decreased on average, but large positive and negative hourly averaged effects were calculated (from −67 to +639xa0%). This explorative study shows that further research should include pathogen inactivation and more detailed probability functions on precipitation, snow, and large-scale circulation.

Integrating interdisciplinary methodologies for One Health: goat farm re-implicated as the probable source of an urban Q fever outbreak, the Netherlands, 2009.

BackgroundIn spring 2008, a goat farm experiencing Q fever abortions (“Farm A”) was identified as the probable source of a human Q fever outbreak in a Dutch town. In 2009, a larger outbreak with 347 cases occurred in the town, despite no clinical Q fever being reported from any local farm.MethodsOur study aimed to identify the source of the 2009 outbreak by applying a combination of interdisciplinary methods, using data from several sources and sectors, to investigate seventeen farms in the area: namely, descriptive epidemiology of notified cases; collation of veterinary data regarding the seventeen farms; spatial attack rate and relative risk analyses; and GIS mapping of farms and smooth incidence of cases. We conducted further spatio-temporal analyses that integrated temporal data regarding date of onset with spatial data from an atmospheric dispersion model with the most highly suspected source at the centre.ResultsOur analyses indicated that Farm A was again the most likely source of infection, with persons living within 1xa0km of the farm at a 46 times larger risk of being a case compared to those living within 5-10xa0km. The spatio-temporal analyses demonstrated that about 60 – 65xa0% of the cases could be explained by aerosol transmission from Farm A assuming emission from week 9; these explained cases lived significantly closer to the farm than the unexplained cases (pu2009=u20090.004). A visit to Farm A revealed that there had been no particular changes in management during the spring/summer of 2009, nor any animal health problems around the time of parturition or at any other time during the year.ConclusionsWe conclude that the probable source of the 2009 outbreak was the same farm implicated in 2008, despite animal health indicators being absent. Veterinary and public health professionals should consider farms with past as well as current history of Q fever as potential sources of human outbreaks.

Explanatory Variables Associated with Campylobacter and Escherichia coli Concentrations on Broiler Chicken Carcasses during Processing in Two Slaughterhouses

This study aimed at identifying explanatory variables that were associated with Campylobacter and Escherichia coli concentrations throughout processing in two commercial broiler slaughterhouses. Quantative data on Campylobacter and E. coli along the processing line were collected. Moreover, information on batch characteristics, slaughterhouse practices, process performance, and environmental variables was collected through questionnaires, observations, and measurements, resulting in data on 19 potential explanatory variables. Analysis was conducted separately in each slaughterhouse to identify which variables were related to changes in concentrations of Campylobacter and E. coli during the processing steps: scalding, defeathering, evisceration, and chilling. Associations with explanatory variables were different in the slaughterhouses studied. In the first slaughterhouse, there was only one significant association: poorer uniformity of the weight of carcasses within a batch with less decrease in E. coli concentrations after defeathering. In the second slaughterhouse, significant statistical associations were found with variables, including age, uniformity, average weight of carcasses, Campylobacter concentrations in excreta and ceca, and E. coli concentrations in excreta. Bacterial concentrations in excreta and ceca were found to be the most prominent variables, because they were associated with concentration on carcasses at various processing points. Although the slaughterhouses produced specific products and had different batch characteristics and processing parameters, the effect of the significant variables was not always the same for each slaughterhouse. Therefore, each slaughterhouse needs to determine its particular relevant measures for hygiene control and process management. This identification could be supported by monitoring changes in bacterial concentrations during processing in individual slaughterhouses. In addition, the possibility that management and food handling practices in slaughterhouses contribute to the differences in bacterial contamination between slaughterhouses needs further investigation.