Campylobacteriosis in Finland: Passive Surveillance in 2004-2021 and a Pilot Case-Control Study with Whole-Genome Sequencing in Summer 2022.

Campylobacteriosis causes a significant disease burden in humans worldwide and is the most common type of zoonotic gastroenteritis in Finland. To identify infection sources for domestic Campylobacter infections, we analyzed Campylobacter case data from the Finnish Infectious Disease Register (FIDR) in 2004-2021 and outbreak data from the National Food- and Waterborne Outbreak Register (FWO Register) in 2010-2021, and conducted a pilot case-control study (256 cases and 756 controls) with source attribution and patient sample analysis using whole-genome sequencing (WGS) in July-August 2022. In the FIDR, 41% of the cases lacked information on travel history. Based on the case-control study, we estimated that of all cases, 39% were of domestic origin. Using WGS, 22 clusters of two or more cases were observed among 185 domestic cases, none of which were reported to the FWO register. Based on this case-control study and source attribution, poultry is an important source of campylobacteriosis in Finland. More extensive sampling and comparison of patient, food, animal, and environmental isolates is needed to estimate the significance of other sources. In Finland, campylobacteriosis is more often of domestic origin than FIDR notifications indicate. To identify the domestic cases, travel information should be included in the FIDR notification, and to improve outbreak detection, all domestic patient isolates should be sequenced.

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.

An openly available wearable, a diaper cover, monitors infant's respiration and position during rest and sleep.

AIM: To describe and test the accuracy of respiratory rate assessment in long-term surveillance using an open-source infant wearable, NAPping PAnts (NAPPA). METHODS: We recorded 24 infants aged 1-9 months using our newly developed infant wearable that is a diaper cover with an integrated programmable electronics with accelerometer and gyroscope sensors. The sensor collects child's respiration rate (RR), activity and body posture in 30-s epochs, to be downloaded afterwards into a mobile phone application. An automated RR quality measure was also implemented using autocorrelation function, and the accuracy of RR estimate was compared with a reference obtained from the simultaneously recorded capnography signal that was part of polysomnography recordings. RESULTS: Altogether 88 h 27 min of data were recorded, and 4147 epochs (39% of all data) were accepted after quality detection. The median of patient wise mean absolute errors in RR estimates was 1.5 breaths per minute (interquartile range 1.1-2.6 bpm), and the Blandt-Altman analysis indicated an RR bias of 0.0 bpm with the 95% limits of agreement of -5.7-5.7 bpm. CONCLUSION: Long-term monitoring of RR and posture can be done with reasonable accuracy in out-of-hospital settings using NAPPA, an openly available infant wearable.

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.

Unmet Rehabilitation Needs after Traumatic Brain Injury across Europe: Results from the CENTER-TBI Study.

This study aims to assess rehabilitation needs and provision of rehabilitation services for individuals with moderate-to-severe disability and investigate factors influencing the probability of receiving rehabilitation within six months after traumatic brain injury (TBI). Overall, the analyses included 1206 individuals enrolled in the CENTER-TBI study with severe-to-moderate disability. Impairments in five outcome domains (daily life activities, physical, cognition, speech/language, and psychological) and the use of respective rehabilitation services (occupational therapy, physiotherapy, cognitive and speech therapies, and psychological counselling) were recorded. Sociodemographic and injury-related factors were used to investigate the probability of receiving rehabilitation. Physiotherapy was the most frequently provided rehabilitation service, followed by speech and occupational therapy. Psychological counselling was the least frequently accessed service. The probability of receiving a rehabilitative intervention increased for individuals with greater brain injury severity (odds ratio (OR) 1.75, CI 95%: 1.27-2.42), physical (OR 1.92, CI 95%: 1.21-3.05) and cognitive problems (OR 4.00, CI 95%: 2.34-6.83) but decreased for individuals reporting psychological problems (OR 0.57, CI 95%: 1.21-3.05). The study results emphasize the need for more extensive prescription of rehabilitation services for individuals with disability. Moreover, targeted rehabilitation programs, which aim to improve outcomes, should specifically involve psychological services to meet the needs of individuals recovering from TBI.

Care transitions in the first 6months following traumatic brain injury: Lessons from the CENTER-TBI study.

BACKGROUND: No large international studies have investigated care transitions during or after acute hospitalisations for traumatic brain injury (TBI). OBJECTIVES: To characterise various TBI-care pathways and the number of associated transitions during the first 6 months after TBI and to assess the impact of these on functional TBI outcome controlled for demographic and injury-related factors. METHODS: This was a cohort study of patients with TBI admitted to various trauma centres enrolled in the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study. Number of transitions and specific care pathways were identified. Multiple logistic regression analyses were used to assess the impact of number of transitions and care pathways on functional outcome at 6 months post-injury as assessed by the Glasgow Outcome Scale-Extended (GOSE). RESULTS: In total, 3133 patients survived the acute TBI-care pathway and had at least one documented in-hospital transition at 6-month follow-up. The median number of transitions was 3 (interquartile range 2-3). The number of transitions did not predict functional outcome at 6 months (odds ratio 1.08, 95% confidence interval 1.09-1.18; P=0.063). A total of 378 different care pathways were identified; 8 were identical for at least 100 patients and characterized as "common pathways". Five of these common care pathways predicted better functional outcomes at 6 months, and the remaining 3 pathways were unrelated to outcome. In both models, increased age, violence as the cause of injury, pre-injury presence of systemic disease, both intracranial and overall injury severity, and regions of Southern/Eastern Europe were associated with unfavourable functional outcomes at 6 months. CONCLUSIONS: A high number of different and complex care pathways was found for patients with TBI, particularly those with severe injuries. This high number and variety of care pathway possibilities indicates a need for standardisation and development of "common data elements for TBI care pathways" for future studies. STUDY REGISTRATION: ClinicalTrials.gov NCT02210221.

Predictors of Access to Rehabilitation in the Year Following Traumatic Brain Injury: A European Prospective and Multicenter Study.

BACKGROUND: Although rehabilitation is beneficial for individuals with traumatic brain injury (TBI), a significant proportion of them do not receive adequate rehabilitation after acute care. OBJECTIVE: Therefore, the goal of this prospective and multicenter study was to investigate predictors of access to rehabilitation in the year following injury in patients with TBI. METHODS: Data from a large European study (CENTER-TBI), including TBIs of all severities between December 2014 and December 2017 were used (N = 4498 patients). Participants were dichotomized into those who had and those who did not have access to rehabilitation in the year following TBI. Potential predictors included sociodemographic factors, psychoactive substance use, preinjury medical history, injury-related factors, and factors related to medical care, complications, and discharge. RESULTS: In the year following traumatic injury, 31.4% of patients received rehabilitation services. Access to rehabilitation was positively and significantly predicted by female sex (odds ratio [OR] = 1.50), increased number of years of education completed (OR = 1.05), living in Northern (OR = 1.62; reference: Western Europe) or Southern Europe (OR = 1.74), lower prehospital Glasgow Coma Scale score (OR = 1.03), higher Injury Severity Score (OR = 1.01), intracranial (OR = 1.33) and extracranial (OR = 1.99) surgery, and extracranial complication (OR = 1.75). On contrast, significant negative predictors were lack of preinjury employment (OR = 0.80), living in Central and Eastern Europe (OR = 0.42), and admission to hospital ward (OR = 0.47; reference: admission to intensive care unit) or direct discharge from emergency room (OR = 0.24). CONCLUSIONS: Based on these findings, there is an urgent need to implement national and international guidelines and strategies for access to rehabilitation after TBI.

An Open Source Classifier for Bed Mattress Signal in Infant Sleep Monitoring.

OBJECTIVE: To develop a non-invasive and clinically practical method for a long-term monitoring of infant sleep cycling in the intensive care unit. METHODS: Forty three infant polysomnography recordings were performed at 1-18 weeks of age, including a piezo element bed mattress sensor to record respiratory and gross-body movements. The hypnogram scored from polysomnography signals was used as the ground truth in training sleep classifiers based on 20,022 epochs of movement and/or electrocardiography signals. Three classifier designs were evaluated in the detection of deep sleep (N3 state): support vector machine (SVM), Long Short-Term Memory neural network, and convolutional neural network (CNN). RESULTS: Deep sleep was accurately identified from other states with all classifier variants. The SVM classifier based on a combination of movement and electrocardiography features had the highest performance (AUC 97.6%). A SVM classifier based on only movement features had comparable accuracy (AUC 95.0%). The feature-independent CNN resulted in roughly comparable accuracy (AUC 93.3%). CONCLUSION: Automated non-invasive tracking of sleep state cycling is technically feasible using measurements from a piezo element situated under a bed mattress. SIGNIFICANCE: An open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant's sleep cycling.

Listeria monocytogenes risk assessment on cold smoked and salt-cured fishery products in Finland - A repeated exposure model.

Listeria monocytogenes causes severe consequences especially for persons belonging to risk groups. Finland is among the countries with highest number of listeriosis cases in the European Union. Although most reported cases appear to be sporadic and the maximum bacterial concentration of 100 cfu/g is not usually exceeded at retail, cold smoked and salt-cured fish products have been noted as those products with great risk especially for the elderly. In order to investigate the listeriosis risk more carefully, an exposure assessment was developed, and laboratory results for cold smoked and salt-cured salmon products were exploited. L. monocytogenes exposure was modeled for consumers in two age groups, the elderly population as a risk group and the working-age population as a reference. Incidence was assessed by estimating bacterial growth in the food products at three temperatures. Bayesian estimation of the risk was based on bacterial occurrence and product consumption data and epidemiological population data. The model builds on a two-state Markov chain describing repeated consumption on consecutive days. The cumulative exposure is probabilistically governed by the daily decreasing likelihood of continued consumption and the increasing bacterial concentrations due to growth. The population risk was then predicted with a Poisson distribution accounting for the daily probabilities of purchasing a contaminated product and the cumulative total probability of infection from its use. According to the model presented in this article, elderly Finns are at a greater risk of acquiring listeriosis than healthy adults. The risk for the elderly does not fully diminish even if the products have been stored at the recommended temperature (between 0 and 3 °C). It can be concluded that the stage after retail, i.e. food handling and storage by consumer or professional kitchens, is essential to protection against listeriosis. The estimation model provides means for assessing the joint impacts of these effects.

A Modular Bayesian Salmonella Source Attribution Model for Sparse Data.

Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were used for a smoothed estimation of the annual relative proportions of different salmonella subtypes in each of the sources. The source-specific effects and the salmonella subtype-specific effects were included in the epidemiological model to describe the differences between sources and between subtypes in their ability to infect humans. The estimation of these parameters was based on data from multiple years. Finally, the model combines the total evidence from different modules to proportion human salmonellosis cases according to their sources. The model was applied to allocate reported human salmonellosis cases from the years 2008 to 2015 to eight food sources.

Longitudinal Study of Shiga Toxin-Producing Escherichia coli and Campylobacter jejuni on Finnish Dairy Farms and in Raw Milk.

Shiga toxin-producing Escherichia coli (STEC) and Campylobacter jejuni are notable health hazards associated with the consumption of raw milk. These bacteria may colonize the intestines of asymptomatic cattle and enter bulk tank milk via fecal contamination during milking. We studied the frequency of STEC O157:H7 and C. jejuni contamination in tank milk (n = 785) and the in-line milk filters of milking machines (n = 631) versus the frequency of isolation from cattle feces (n = 257) on three Finnish dairy farms for 1 year. Despite simultaneous isolation of STEC O157:H7 (17%) or C. jejuni (53%) from cattle, these bacteria were rarely isolated from milk filters (2% or <1%, respectively) and milk (0%). As revealed by phylogenomics, one STEC O157:H7 strain at a time was detected on each farm and persisted for ≤12 months despite rigorous hygienic measures. C. jejuni strains of a generalist sequence type (ST-883 and ST-1080) persisted in the herds for ≥11 months, and several other C. jejuni types were detected sporadically. The stx gene carried by STEC was detected more frequently from milk filters (37%) than from milk (7%), suggesting that milk filters are more suitable sampling targets for monitoring than milk. A questionnaire of on-farm practices suggested lower stx contamination of milk when major cleansing in the barn, culling, or pasturing of dairy cows was applied, while a higher average outdoor temperature was associated with higher stx contamination. Because pathogen contamination occurred despite good hygiene and because pathogen detection from milk and milk filters proved challenging, we recommend heat treatment for raw milk before consumption.IMPORTANCE The increased popularity of raw milk consumption has created demand for relaxing legislation, despite the risk of contamination by pathogenic bacteria, notably STEC and C. jejuni However, the epidemiology of these milk-borne pathogens on the herd level is still poorly understood, and data are lacking on the frequency of milk contamination on farms with cattle shedding these bacteria in their feces. This study suggests (i) that STEC contamination in milk can be reduced, but not prevented, by on-farm hygienic measures while fecal shedding is observable, (ii) that milk filters are more suitable sampling targets for monitoring than milk although pathogen detection from both sample matrices may be challenging, and (iii) that STEC and C. jejuni genotypes may persist in cattle herds for several months. The results can be utilized in developing and targeting pathogen monitoring and risk management on the farm level and contributed to the revision of Finnish legislation in 2017.

Validation of EN ISO method 10273 - Detection of pathogenic Yersinia enterocolitica in foods.

EN ISO 10273 method for the detection of pathogenic Yersinia enterocolitica in foods was validated in the project Mandate M/381 funded by European Commission. A total of 14 laboratories from five European countries participated in the interlaboratory study (ILS) organized during 2013 and 2014. Before the ILS, the method was revised by an international group of experts and the performance of the revised method was assessed in an ILS study. The results are published as a part of the standard EN ISO 10273 revision. The study included three rounds with different sample types; raw milk, iceberg lettuce and minced meat, inoculated with a low and high level of pathogenic Y. enterocolitica strains representing major pathogenic bioserotypes 4/O:3 and 2/O:9. The homogeneity and stability of the samples were verified before dispatching them to the laboratories. The results demonstrated the method sensitivity of 96% in raw milk, 97% in minced meat, and 98% in lettuce at high inoculation level of pathogenic Y. enterocolitica. The specificity was 100% in raw milk, 96% in minced meat, and 98% in lettuce. The level of detection, LOD50, varied between study rounds, being 9.4 CFU/25 ml in raw milk, 9.9 CFU/25 g in minced meat and 63 CFU/25 g in lettuce samples. During the study, confirmation by using real-time PCR method ISO/TS 18867 together with pyrazinamidase testing was also validated, as alternative to conventional biochemical confirmation. When comparing different isolation steps used in the revised method during the study rounds, PSB enrichment and plating on CIN after alkaline (KOH) treatment showed the highest sensitivity (52-92%) in raw milk and minced meat samples. In lettuce samples, however, ITC with KOH treatment before plating on CIN showed higher sensitivity (64% at low level; 82% at high level) than plating on CIN from PSB with KOH treatment (44% at low level; 74% at high level). Statistical analysis of different isolation steps supported the use of two enrichment media, PSB and ITC, in the revised method. Recovery of pathogenic Y. enterocolitica on CIN was most efficient after KOH treatment and, based on the analysis, plating on CIN agar without KOH treatment could be left as optional procedure in the method.

Bayesian model for tracing Salmonella contamination in the pig feed chain.

Salmonella infections in pigs are in most cases asymptomatic, posing a risk of salmonellosis for pork consumers. Salmonella can transmit to pigs from various sources, including contaminated feed. We present an approach for quantifying the risk to pigs from contaminations in the feed chain, based on a Bayesian model. The model relies on Salmonella surveillance data and other information from surveys, reports, registries, statistics, legislation and literature regarding feed production and pig farming. Uncertainties were probabilistically quantified by synthesizing evidence from the available information over a categorically structured flow chain of ingredients mixed for feeds served to pigs. Model based probability for infection from feeds together with Salmonella subtyping data, were used to estimate the proportion of Salmonella infections in pigs attributable to feed. The results can be further used in assessments considering the human health risk linked to animal feed via livestock. The presented methods can be used to predict the effect of changes in the feed chain, and they are generally applicable to other animals and pathogens.

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.

Quantitative risk assessment on the dietary exposure of Finnish children and adults to nitrite.

Nitrite intake from the consumption of cured meat and tap water was estimated for Finnish children of 1, 3 and 6 years as well as Finnish adults of 25-74 years. Nitrite content in the foods was measured by capillary electrophoresis, and was then used together with individual food consumption data from the FINDIET 2007 and DIPP studies in a stochastic exposure assessment by a Monte Carlo Risk Assessment (MCRA) program. Nitrite intake from additive sources and tap water was assessed, and more than every 10th child between the ages 3 and 6 years was estimated to have a nitrite intake exceeding the acceptable daily intake (ADI) of nitrite. The high exposure levels were caused by frequent consumption of large portions of sausages, up to 350 g day(-1) or 750 g in 3 days, among the children. Median nitrite intake from cured meat was 0.016, 0.040, 0.033 and 0.005 mg kg(-1) body weight day(-1) for children of 1, 3 and 6 years and adults, respectively. Bayesian estimation was employed to determine safe consumption levels of sausages and cold cuts for children, and these results gave rise to new national food consumption advice.

Bayesian modeling of reproducibility and robustness of RNA reverse transcription and quantitative real-time polymerase chain reaction.

Gene expression measurements with quantitative real-time polymerase chain reaction (PCR) have a major hindrance of not directly measuring RNA and, thus, needing a reverse transcription (RT) step to first produce complementary DNA (cDNA). However, few studies have assessed the robustness of the RT step, and almost none has compared the factual performance of RT and real-time PCR enzymes. Here, we examined the variability of RT and PCR reactions and compared enzyme reproducibility by reverse transcribing identical RNA with eight RT enzymes and then amplifying the cDNA produced by one of them with six real-time PCR enzymes. The same four reference genes were measured in both experiments, and the data were analyzed with Bayesian multilevel models. Reproducibility and the efficiency of the RT enzymes, excluding one, varied moderately, but RT was always less precise than PCR; four PCR enzymes performed in an excellent manner. The transcription efficiencies of two of the measured reference genes (Actb and Sdha) lacked covariance with the general RT efficiency and showed poor reproducibility. In conclusion, most variation in quantitative real-time RT-PCR (RT-qPCR) emanates from the RT phase, and gene-related factors seem to be the primary determinants of this variation, discouraging the use of control genes in RT-qPCR normalization without prior information of their RT robustness.

Bayesian temporal source attribution of foodborne zoonoses: Campylobacter in Finland and Norway.

Statistical source attribution approaches of food-related zoonoses can generally be based on reported diagnosed human cases and surveillance results from different food sources or reservoirs of bacteria. The attribution model, or probabilistic classifier, can thus be based on the (sub)typing information enabling comparison between human infections and samples derived from source surveillance. Having time series of both data allows analyzing temporal patterns over time providing a repeated natural experiment. A Bayesian approach combining both sources of information over a long time series is presented in the case of Campylobacter in Finland and Norway. The full model is transparently presented and derived from the Bayes theorem. Previous statistical source attribution approaches are here advanced (1) by explicit modeling of the cases not associated with any of the sources under surveillance over time, (2) by modeling uncertain prevalence in a food source by bacteria type over time, and (3) by implementing formal model fit assessment using posterior predictive discrepancy functions. Large proportion of all campylobacteriosis can be attributed to broiler, but considerable uncertainty remains over time. The source attribution is inherently incomplete if only the sources under surveillance are included in the model. All statistical source attribution approaches should include a model fit assessment for judgment of model performance with respect to relevant quantities of interest. It is especially relevant when the model aims at a synthesis of several incomplete information sources under significant uncertainty of explanatory variables.

Listeria monocytogenes contamination in pork can originate from farms.

The presence of Listeria monocytogenes in the pork production chain was followed from farm to slaughterhouse by examining the farm and slaughterhouse levels in the same 364 pigs, and finally by analyzing the cut meats from the same pig lots. Both organic and conventional farms were included in the study. Altogether, 1,962 samples were collected, and the 424 L. monocytogenes isolates were analyzed by pulsed-field gel electrophoresis. The results from microbial analyses were combined with data from an on-farm observation and a questionnaire to clarify the associations between farm factors and prevalence of L. monocytogenes. The prevalence of L. monocytogenes was 11, 1, 1, 24, 5, 1, and 4% in feed and litter, rectal swabs, intestinal contents, tonsils, pluck sets (including lungs, heart, liver, and kidney), carcasses, and meat cuts, respectively. The prevalence was significantly higher in organic than in conventional pig production at the farm and slaughterhouse level, but not in meat cuts. Similar L. monocytogenes genotypes were recovered in different steps of the production chain in pigs originating from the same farm. Specific farm management factors, i.e., large group size, contact with pet and pest animals, manure treatment, use of coarse feed, access to outdoor area, hygiene practices, and drinking from the trough, influenced the presence of L. monocytogenes in pigs. L. monocytogenes was present in the production chain, and transmission of the pathogen was possible throughout the chain, from the farm to pork. Good farm-level practices can therefore be utilized to reduce the prevalence of this pathogen.

Reduction of enteropathogenic Yersinia in the pig slaughterhouse by using bagging of the rectum.

To evaluate the effectiveness of bagging of the rectum in mitigating the contamination of carcasses with enteropathogenic Yersinia at the slaughterhouse and to estimate the hidden prevalences of these pathogens in different farm types and capacities, samples from pigs, carcasses, and slaughterhouse environment were collected, and a Bayesian probability model was constructed. In addition, the contamination routes were studied with molecular typing of the isolated strains. According to the model, bagging of the rectum reduced carcass contamination significantly with pathogenic Yersinia enterocolitica, but not with Yersinia pseudotuberculosis, and alone it was insufficient to completely prevent the carcass contamination with enteropathogenic Yersinia. The hidden prevalence of pathogenic Y. enterocolitica was higher at high production capacity than it was in low production capacity, but the 95 % credible intervals overlapped. Slaughterhouse environments can contaminate carcasses with enteropathogenic Yersinia, but the plausible main contamination source is the pig carrying the pathogen.

Contamination of carcasses with human pathogenic Yersinia enterocolitica 4/O:3 originates from pigs infected on farms.

Pigs are considered as a major reservoir of human pathogenic Yersinia enterocolitica and a source of human yersiniosis. However, the transmission route of Y. enterocolitica from farm to pork is still unclear. The transmission of pathogenic Y. enterocolitica from pigs to carcasses and pluck sets was investigated by collecting samples from 364 individual ear-tagged pigs on the farm and at the slaughterhouse. In addition, isolated strains were analyzed, using pulsed-field gel electrophoresis. Isolation of similar genotypes of pathogenic Y. enterocolitica 4/O:3 in animals on the farm and at the slaughterhouse and in carcasses shows that carcass contamination originates from the strains a pig carries during the fattening period. Direct contamination from the carrier pig to its subsequent pluck set is also the primary contamination route for pluck sets, but cross-contamination appears to have a larger impact on pluck set contamination than on carcasses. In this study, the within-farm prevalence of pathogenic Y. enterocolitica varied from 0% to 100%, indicating specific farm factors affect the prevalence of Y. enterocolitica in pigs. The association of farm factors with the high prevalence of pathogenic Y. enterocolitica on farms was studied for the first time, using correlation and two-level logistic regression analyses. Specific farm factors, i.e. drinking from a nipple, absence of coarse feed or bedding for slaughter pigs, and no access of pest animals to pig house, were associated with a high prevalence of pathogenic Y. enterocolitica 4/O:3.