Attribution of human Salmonella infections to animal and food sources in Italy (2002-2010): adaptations of the Dutch and modified Hald source attribution models

Livestock-associated spatial risk factors for human salmonellosis and campylobacteriosis

AIMS

Most human infections with non-typhoid Salmonella (NTS) or Campylobacter are zoonotic in nature and acquired though consumption of contaminated food of mainly animal origin. However, individuals may also acquire salmonellosis or campylobacteriosis through non-foodborne transmission pathways, such as those mediated by the environment. This emphasizes the need to consider both direct and indirect exposure to livestock sources as a possible transmission route for NTS and Campylobacter. Therefore, this study aimed at assessing whether salmonellosis and campylobacteriosis incidence is spatially associated with exposure to livestock (i.e. small ruminants, dairy cows, veal calves, laying hens, broiler chickens and pigs) in the Netherlands for the years 2007-2019 and 2014-2019 respectively.

METHODS AND RESULTS

Risk factors (population-weighted number of animals) and their population attributable fractions were determined using a Poisson regression model with a log-link function fitted using integrated nested Laplace approximation. The analyses were performed for different hexagonal sizes (90, 50, 25 and 10 km2) and accounted for geographical coverage of the diagnostic laboratory catchment areas. Moreover, serological data were used to look into the possible effects of acquired immunity due to repeated exposure to the pathogen through the environment that would potentially hinder the analyses based on the incidence of reported cases. A linear mixed-effects model was then fitted where the postal code areas were included as a random effect. Livestock was not consistently significantly associated with acquiring salmonellosis or campylobacteriosis in the Netherlands.

CONCLUSIONS

Results showed that living in livestock-rich areas in the Netherlands is not a consistently significant, spatially restricted risk factor for acquiring salmonellosis or campylobacteriosis, thereby supporting current knowledge that human infections with Salmonella and Campylobacter are mainly foodborne.

Source attribution of campylobacteriosis in Australia, 2017-2019

Campylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. Campylobacter colonizes a variety of warm-blooded hosts that are reservoirs for human campylobacteriosis. The proportions of Australian cases attributable to different animal reservoirs are unknown but can be estimated by comparing the frequency of different sequence types in cases and reservoirs. Campylobacter isolates were obtained from notified human cases and raw meat and offal from the major livestock in Australia between 2017 and 2019. Isolates were typed using multi-locus sequence genotyping. We used Bayesian source attribution models including the asymmetric island model, the modified Hald model, and their generalizations. Some models included an "unsampled" source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. Model fits were compared using the Watanabe-Akaike information criterion. We included 612 food and 710 human case isolates. The best fitting models attributed >80% of Campylobacter cases to chickens, with a greater proportion of C. coli (>84%) than C. jejuni (>77%). The best fitting model that included an unsampled source attributed 14% (95% credible interval [CrI]: 0.3%-32%) to the unsampled source and only 2% to ruminants (95% CrI: 0.3%-12%) and 2% to pigs (95% CrI: 0.2%-11%) The best fitting model that did not include an unsampled source attributed 12% to ruminants (95% CrI: 1.3%-33%) and 6% to pigs (95% CrI: 1.1%-19%). Chickens were the leading source of human Campylobacter infections in Australia in 2017-2019 and should remain the focus of interventions to reduce burden.

Quantitative Risk Assessment for the Introduction of Carbapenem-Resistant Enterobacteriaceae (CPE) into Dutch Livestock Farms

Early detection of emerging carbapenem-resistant Enterobacteriaceae (CPE) in food-producing animals is essential to control the spread of CPE. We assessed the risk of CPE introduction from imported livestock, livestock feed, companion animals, hospital patients, and returning travelers into livestock farms in The Netherlands, including (1) broiler, (2) broiler breeder, (3) fattening pig, (4) breeding pig, (5) farrow-to-finish pig, and (6) veal calf farms. The expected annual number of introductions was calculated from the number of farms exposed to each CPE source and the probability that at least one animal in an exposed farm is colonized. The total number of farms with CPE colonization was estimated to be the highest for fattening pig farms, whereas the probability of introduction for an individual farm was the highest for broiler farms. Livestock feed and imported livestock are the most likely sources of CPE introduction into Dutch livestock farms. Sensitivity analysis indicated that the number of fattening pig farms determined the number of high introductions in fattening pigs from feed, and that uncertainty on CPE prevalence impacted the absolute risk estimate for all farm types. The results of this study can be used to inform risk-based surveillance for CPE in livestock farms.

Source attribution of salmonellosis by time and geography in New South Wales, Australia

BACKGROUND

Salmonella is a major cause of zoonotic illness around the world, arising from direct or indirect contact with a range of animal reservoirs. In the Australian state of New South Wales (NSW), salmonellosis is believed to be primarily foodborne, but the relative contribution of animal reservoirs is unknown.

METHODS

The analysis included 4543 serotyped isolates from animal reservoirs and 30,073 serotyped isolates from domestically acquired human cases in NSW between January 2008 and August 2019. We used a Bayesian source attribution methodology to estimate the proportion of foodborne Salmonella infections attributable to broiler chickens, layer chickens, ruminants, pigs, and an unknown or unsampled source. Additional analyses included covariates for four time periods and five levels of rurality.

RESULTS

A single serotype, S. Typhimurium, accounted for 65-75% of included cases during 2008-2014 but < 50% during 2017-2019. Attribution to layer chickens was highest during 2008-2010 (48.7%, 95% CrI 24.2-70.3%) but halved by 2017-2019 (23.1%, 95% CrI 5.7-38.9%) and was lower in the rural and remote populations than in the majority urban population. The proportion of cases attributed to the unsampled source was 11.3% (95% CrI 1.2%-22.1%) overall, but higher in rural and remote populations. The proportion of cases attributed to pork increased from approximately 20% in 2009-2016 to approximately 40% in 2017-2019, coinciding with a rise in cases due to Salmonella ser. 4,5,12:i:-.

CONCLUSION

Layer chickens were likely the primary reservoir of domestically acquired Salmonella infections in NSW circa 2010, but attribution to the source declined contemporaneously with increased vaccination of layer flocks and tighter food safety regulations for the handling of eggs.

Bayesian Source Attribution of Salmonella Typhimurium Isolates From Human Patients and Farm Animals in England and Wales

The purpose of the study was to apply a Bayesian source attribution model to England and Wales based data on Salmonella Typhimurium (ST) and monophasic variants (MST), using different subtyping approaches based on sequence data. The data consisted of laboratory confirmed human cases and mainly livestock samples collected from surveillance or monitoring schemes. Three different subtyping methods were used, 7-loci Multi-Locus Sequence Typing (MLST), Core-genome MLST, and Single Nucleotide Polymorphism distance, with the impact of varying the genetic distance over which isolates would be grouped together being varied for the latter two approaches. A Bayesian frequency matching method, known as the modified Hald method, was applied to the data from each of the subtyping approaches. Pigs were found to be the main contributor to human infection for ST/MST, with approximately 60% of human cases attributed to them, followed by other mammals (mostly horses) and cattle. It was found that the use of different clustering methods based on sequence data had minimal impact on the estimates of source attribution. However, there was an impact of genetic distance over which isolates were grouped: grouping isolates which were relatively closely related increased uncertainty but tended to have a better model fit.

Non-typhoidal human salmonellosis in Rio Grande do Sul, Brazil: A combined source attribution study of microbial subtyping and outbreak data

Salmonella spp. remains the most significant foodborne pathogen in south Brazil, but its epidemiology tends to change over time. Using official and surrogate data, a microbial subtyping model attributed different Salmonella serovars to laying hens, pigs, broilers, and turkeys from 2005 to 2015 in Rio Grande do Sul (RS). Additional to the subtyping model, three sub-analyses of outbreak data attributed Salmonella spp. in humans to animal and non-animal food. Laying hens/eggs was the most important source of human salmonellosis in RS, with almost 40% (159 cases; 95% credibility interval, 43-247) attribution proportion, followed by pigs reared in Santa Catarina, a neighbor state (34.5%). The Salmonella serovars Enteritidis and Typhimurium were the most common serovars involved. Source-related parameters had wide credibility intervals but showed a higher risk of illness from contaminated eggs than from the other three animal-food sources. Analysis of the outbreak data corroborated the findings and indicated signs of decreasing importance for eggs and increasing importance for pork consumption.

Network Approach to Source Attribution of Salmonella enterica Serovar Typhimurium and Its Monophasic Variant

Salmonella enterica subspecies enterica serovar Typhimurium and its monophasic variant are among the most common Salmonella serovars associated with human salmonellosis each year. Related infections are often due to consumption of contaminated meat of pig, cattle, and poultry origin. In order to evaluate novel microbial subtyping methods for source attribution, an approach based on weighted networks was applied on 141 human and 210 food and animal isolates of pigs, broilers, layers, ducks, and cattle collected in Denmark from 2013 to 2014. A whole-genome SNP calling was performed along with cgMLST and wgMLST. Based on these genomic input data, pairwise distance matrices were built and used as input for construction of a weighted network where nodes represent genomes and links to distances. Analyzing food and animal Typhimurium genomes, the coherence of source clustering ranged from 89 to 90% for animal source, from 84 to 85% for country, and from 63 to 65% for year of isolation and was equal to 82% for serotype, suggesting animal source as the first driver of clustering formation. Adding human isolate genomes to the network, a percentage between 93.6 and 97.2% clustered with the existing component and only a percentage between 2.8 and 6.4% appeared as not attributed to any animal sources. The majority of human genomes were attributed to pigs with probabilities ranging from 83.9 to 84.5%, followed by broilers, ducks, cattle, and layers in descending order. In conclusion, a weighted network approach based on pairwise SNPs, cgMLST, and wgMLST matrices showed promising results for source attribution studies.

Global and regional source attribution of Shiga toxin-producing Escherichia coli infections using analysis of outbreak surveillance data

Shiga toxin-producing Escherichia coli (STEC) infections pose a substantial health and economic burden worldwide. To target interventions to prevent foodborne infections, it is important to determine the types of foods leading to illness. Our objective was to determine the food sources of STEC globally and for the six World Health Organization regions. We used data from STEC outbreaks that have occurred globally to estimate source attribution fractions. We categorised foods according to their ingredients and applied a probabilistic model that used information on implicated foods for source attribution. Data were received from 27 countries covering the period between 1998 and 2017 and three regions: the Americas (AMR), Europe (EUR) and Western-Pacific (WPR). Results showed that the top foods varied across regions. The most important sources in AMR were beef (40%; 95% Uncertainty Interval 39-41%) and produce (35%; 95% UI 34-36%). In EUR, the ranking was similar though with less marked differences between sources (beef 31%; 95% UI 28-34% and produce 30%; 95% UI 27-33%). In contrast, the most common source of STEC in WPR was produce (43%; 95% UI 36-46%), followed by dairy (27%; 95% UI 27-27%). Possible explanations for regional variability include differences in food consumption and preparation, frequency of STEC contamination, the potential of regionally predominant STEC strains to cause severe illness and differences in outbreak investigation and reporting. Despite data gaps, these results provide important information to inform the development of strategies for lowering the global burden of STEC infections.

Attribution of Listeria monocytogenes human infections to food and animal sources in Northern Italy

Listeriosis is a foodborne illness characterized by a relatively low morbidity, but a large disease burden due to the severity of clinical manifestations and the high case fatality rate. Increased listeriosis notifications have been observed in Europe since the 2000s. However, the reasons for this increase are largely unknown, with the sources of sporadic human listerioris often remaining elusive. Here we inferred the relative contributions of several putative sources of Listeria monocytogenes strains from listerioris patients in Northern Italy (Piedmont and Lombardy regions), using two established source attribution models (i.e. 'Dutch' and 'STRUCTURE') in comparative fashion. We compared the Multi-Locus Sequence Typing and Multi-Virulence-Locus Sequence Typing profiles of strains collected from beef, dairy, fish, game, mixed foods, mixed meat, pork, and poultry. Overall, 634 L. monocytogenes isolates were collected from 2005 to 2016. In total, 40 clonal complexes and 51 virulence types were identified, with 36% of the isolates belonging to possible epidemic clones (i.e. genetically related strains from unrelated outbreaks). Source attribution analysis showed that 50% of human listerioris cases (95% Confidence Interval 44-55%) could be attributed to dairy products, followed by poultry and pork (15% each), and mixed foods (15%). Since the contamination of dairy, poultry and pork products are closely linked to primary production, expanding actions currently limited to ready-to-eat products to the reservoir level may help reducing the risk of cross-contamination at the consumer level.

Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms

This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne pathogens. WGS offers the highest level of bacterial strain discrimination for food‐borne outbreak investigation and source‐attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial‐temporal factors and the detection of multidirectional transmission and pathogen–host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non‐culturable, difficult‐to‐culture or slow‐growing microorganisms, for tracking of hazard‐related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin‐producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS‐based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross‐border outbreaks and for the development of international standardised risk assessments of food‐borne microorganisms.

Critical Orientation in the Jungle of Currently Available Methods and Types of Data for Source Attribution of Foodborne Diseases

With increased interest in source attribution of foodborne pathogens, there is a need to sort and assess the applicability of currently available methods. Herewith we reviewed the most frequently applied methods for source attribution of foodborne diseases, discussing their main strengths and weaknesses to be considered when choosing the most appropriate methods based on the type, quality, and quantity of data available, the research questions to be addressed, and the (epidemiological and microbiological) characteristics of the pathogens in question. A variety of source attribution approaches have been applied in recent years. These methods can be defined as top–down, bottom–up, or combined. Top–down approaches assign the human cases back to their sources of infection based on epidemiological (e.g., outbreak data analysis, case-control/cohort studies, etc.), microbiological (i.e., microbial subtyping), or combined (e.g., the so-called ‘source-assigned case-control study’ design) methods. Methods based on microbial subtyping are further differentiable according to the modeling framework adopted as frequency-matching (e.g., the Dutch and Danish models) or population genetics (e.g., Asymmetric Island Models and STRUCTURE) models, relying on the modeling of either phenotyping or genotyping data of pathogen strains from human cases and putative sources. Conversely, bottom–up approaches like comparative exposure assessment start from the level of contamination (prevalence and concentration) of a given pathogen in each source, and then go upwards in the transmission chain incorporating factors related to human exposure to these sources and dose-response relationships. Other approaches are intervention studies, including ‘natural experiments,’ and expert elicitations. A number of methodological challenges concerning all these approaches are discussed. In absence of an universally agreed upon ‘gold’ standard, i.e., a single method that satisfies all situations and needs for all pathogens, combining different approaches or applying them in a comparative fashion seems to be a promising way forward.

The role of parameterization in comparing source attribution models based on microbial subtyping for salmonellosis

Source attribution methods attribute human cases of a zoonotic disease to a certain source putatively responsible for this disease. Identifying and quantifying the contribution of different sources to human infections is important for taking appropriate actions for reducing the exposure of the consumer to zoonotic pathogens. One widely used method is the microbial subtyping approach, whose principle is to compare the frequency of pathogen subtypes from different sources (e.g. animals or food) with the frequency of these subtypes in human cases. This paper studies the relationship between a Bayesian microbial subtyping approach described by Hald and coworkers subsequently modified by David and coworkers, here called the Hald model, and a frequentist approach known as the "Dutch model." The comparison between the Bayesian and frequentist model is done for two data sets on salmonellosis in Germany from different time periods (year 2004-2007 and 2010-2011). The results of both approaches are in good agreement with each other for the used data. It is shown here mathematically that a certain parameterization can be used to transform the probabilistic Hald model into a deterministic form, which is equivalent to the Dutch model. That certain parameterization secures independence of the model outcomes from the choice of so-called unique subtypes (which are unique in the sense that they are found exclusively in one of the sources). It is shown that deviating from that certain parameterization leads variations in the model outcome dependent on which unique subtypes are chosen in the process of modelling.

Source Attribution and Risk Assessment of Antimicrobial Resistance

Source attribution and microbial risk assessment methods have been widely applied for the control of several foodborne pathogens worldwide by identifying (i) the most important pathogen sources and (ii) the risk represented by specific foods and the critical points in these foods' production chains for microbial control. Such evidence has proved crucial for risk managers to identify and prioritize effective food safety and public health strategies. In the context of antimicrobial resistance (AMR) from livestock and pets, the utility of these methods is recognized, but a number of challenges have largely prevented their application and routine use. One key challenge has been to define the hazard in question: Is it the antimicrobial drug use in animals, the antimicrobial-resistant bacteria in animals and foods, or the antimicrobial resistance genes that can be transferred between commensal and pathogenic bacteria in the animal or human gut or in the environment? Other important limitations include the lack of occurrence and transmission data and the lack of evidence to inform dose-response relationships. We present the main principles, available methods, strengths, and weaknesses of source attribution and risk assessment methods, discuss their utility to identify sources and estimate risks of AMR from livestock and pets, and provide an overview of conducted studies. In addition, we discuss remaining challenges and current and future opportunities to improve methods and knowledge of the sources and transmission routes of AMR from animals through food, direct contact, or the environment, including improvements in surveillance and developments in genotypic typing methods.

Quantitative Microbial Risk Assessment of Salmonellosis from the Consumption of Australian Pork: Minced Meat from Retail to Burgers Prepared and Consumed at Home

Pork burgers could be expected to have an elevated risk of salmonellosis compared to other pork products due to their comminuted nature. A stochastic risk assessment was performed to estimate the risk of salmonellosis from Australian pork burgers and considered risk-affecting factors in the pork supply chain from retail to consumption at home. Conditions modeled included prevalence and concentration of Salmonella in pork mince, time and temperature effects during retail, consumer transport, and domestic storage and the effect of cooking, with the probability of illness from consumption estimated based on these effects. The model was two-dimensional, allowing for the separation of variability and uncertainty. Potential changes to production practices and consumer behaviors were examined through alternative scenarios. Under current conditions in Australia, the mean risk of salmonellosis from consumption of 100 g pork burgers was estimated to be 1.54 × 10 - 8 per serving or one illness per 65,000,000 servings consumed. Under a scenario in which all pork mince consumed is served as pork burgers, and with conservative (i.e., worst-case) assumptions, 0.746 cases of salmonellosis per year from pork burgers in Australia were predicted. Despite the adoption of several conservative assumptions to fill data gaps, it is predicted that pork burgers have a low probability of causing salmonellosis in Australia.

Salmonella serovar distribution from non-human sources in Italy; results from the IT-Enter-Vet network

The study summarises the results obtained over the period 2002-2013 by the Italian IT-Enter-Vet network, aimed at collecting data on Salmonella isolates from non-human sources. A total of 42,491 Salmonella isolates were reported with a progressive decrease over the years. S. Typhimurium was the most frequent serovar up to 2011, but then, it was overtaken by S. 4,[5],12,:i:-, S. Derby, S. Livingstone and S. Enteritidis alternated as the third most commonly isolated serovars. With regard to the sources of isolation, S. Typhimurium was distributed ubiquitously among the animal species. On the contrary, S. 4,[5],12,:i:- and S. Derby were strictly associated with pigs, whereas S. Livingstone, S. Enteritidis and S. Infantis were clearly related to poultry. Intriguingly, when the frequency of serovar distribution along the food chain was considered, it was evident that S. Typhimurium and S. Derby tended to persist along the chain, as they were isolated even more frequently from foods than from animals. A similar distribution was found for S. Enteritidis and S. Hadar. Despite limitations related to non-mandatory participation of laboratories in the network, the data presented are valuable to obtain a picture of the evolution of Salmonella from non-human sources over time in Italy.

Attribution of human infections with Shiga toxin-producing Escherichia coli (STEC) to livestock sources and identification of source-specific risk factors, The Netherlands (2010-2014)

Shiga toxin-producing Escherichia coli (STEC) is a zoonotic pathogen of public health concern whose sources and transmission routes are difficult to trace. Using a combined source attribution and case-control analysis, we determined the relative contributions of four putative livestock sources (cattle, small ruminants, pigs, poultry) to human STEC infections and their associated dietary, animal contact, temporal and socio-econo-demographic risk factors in the Netherlands in 2010/2011-2014. Dutch source data were supplemented with those from other European countries with similar STEC epidemiology. Human STEC infections were attributed to sources using both the modified Dutch model (mDM) and the modified Hald model (mHM) supplied with the same O-serotyping data. Cattle accounted for 48.6% (mDM) and 53.1% (mHM) of the 1,183 human cases attributed, followed by small ruminants (mDM: 23.5%; mHM: 25.4%), pigs (mDM: 12.5%; mHM: 5.7%) and poultry (mDM: 2.7%; mHM: 3.1%), whereas the sources of the remaining 12.8% of cases could not be attributed. Of the top five O-serotypes infecting humans, O157, O26, O91 and O103 were mainly attributed to cattle (61%-75%) and O146 to small ruminants (71%-77%). Significant risk factors for human STEC infection as a whole were the consumption of beef, raw/undercooked meat or cured meat/cold cuts. For cattle-attributed STEC infections, specific risk factors were consuming raw meat spreads and beef. Consuming raw/undercooked or minced meat were risk factors for STEC infections attributed to small ruminants. For STEC infections attributed to pigs, only consuming raw/undercooked meat was significant. Consuming minced meat, raw/undercooked meat or cured meat/cold cuts were associated with poultry-attributed STEC infections. Consuming raw vegetables was protective for all STEC infections. We concluded that domestic ruminants account for approximately three-quarters of reported human STEC infections, whereas pigs and poultry play a minor role and that risk factors for human STEC infection vary according to the attributed source.

Foodborne Salmonellosis in Italy: Characterization of Salmonella enterica Serovar Typhimurium and Monophasic Variant 4,[5],12:i- Isolated from Salami and Human Patients

Salmonella enterica serovar Typhimurium (STm) and its monophasic variant 4,[5],12:i:- (VMSTm) have been responsible for an increased number of foodborne infections in humans in Europe in recent years. The aim of this study was to investigate the origin of three foodborne salmonellosis outbreaks that occurred in Pavia Province (Lombardy region, northern Italy) in 2010. Phenotypic and genetic characteristics of the STm and VMSTm isolates from patients and from food that were recovered in the framework of the three outbreaks were evaluated through serotyping, phage typing, antimicrobial susceptibility testing, pulsed-field gel electrophoresis (PFGE), and multiple-locus variable-number tandem repeat analysis (MLVA). Salami from three artisan producers, which had all purchased meat from the same slaughterhouse, was the food source of infection in outbreak I. STm isolates were recovered from salami and patients with symptoms of gastroenteritis. These isolates had the same PFGE type and the same rare MLVA profile (3-18-9-NA-211). The same molecular profiles were found in an STm isolate from a salami, which likely was the source of another family outbreak (II). A VMSTm strain with common phenotypic and molecular profiles was isolated from three hospitalized patients and identified as the cause of another putative outbreak (III). During the following 3 years (2011 through 2013), 360 salami produced in Pavia Province were monitored for the presence of S. enterica . In 2011, no STm and VMSTm isolates were recovered from 159 salami tested. During 2012 and 2013, 13.9% of 201 tested salami harbored S. enterica , and half of the isolates were VMSTm, mainly in salami from those artisan producers involved in the previous outbreaks. These isolates were genetically variable, especially in terms of MLVA profiles. The data collected suggest that from 2012, VMSTm has replaced STm in the environments of the salami producers monitored in this study, and these data confirm the dominance of this emergent serovar along the pork supply chain.

New paradigms for Salmonella source attribution based on microbial subtyping

Microbial subtyping is the most common approach for Salmonella source attribution. Typically, attributions are computed using frequency-matching models like the Dutch and Danish models based on phenotyping data (serotyping, phage-typing, and antimicrobial resistance profiling). Herewith, we critically review three major paradigms facing Salmonella source attribution today: (i) the use of genotyping data, particularly Multi-Locus Variable Number of Tandem Repeats Analysis (MLVA), which is replacing traditional Salmonella phenotyping beyond serotyping; (ii) the integration of case-control data into source attribution to improve risk factor identification/characterization; (iii) the investigation of non-food sources, as attributions tend to focus on foods of animal origin only. Population genetics models or simplified MLVA schemes may provide feasible options for source attribution, although there is a strong need to explore novel modelling options as we move towards whole-genome sequencing as the standard. Classical case-control studies are enhanced by incorporating source attribution results, as individuals acquiring salmonellosis from different sources have different associated risk factors. Thus, the more such analyses are performed the better Salmonella epidemiology will be understood. Reparametrizing current models allows for inclusion of sources like reptiles, the study of which improves our understanding of Salmonella epidemiology beyond food to tackle the pathogen in a more holistic way.

Increase in reptile-associated human salmonellosis and shift toward adulthood in the age groups at risk, the Netherlands, 1985 to 2014

While the contribution of the main food-related sources to human salmonellosis is well documented, knowledge on the contribution of reptiles is limited. We quantified and examined trends in reptile-associated salmonellosis in the Netherlands during a 30-year period, from 1985 to 2014. Using source attribution analysis, we estimated that 2% (95% confidence interval: 1.3–2.8) of all sporadic/domestic human salmonellosis cases reported in the Netherlands during the study period (n = 63,718) originated from reptiles. The estimated annual fraction of reptile-associated salmonellosis cases ranged from a minimum of 0.3% (corresponding to 11 cases) in 1988 to a maximum of 9.3% (93 cases) in 2013. There was a significant increasing trend in reptile-associated salmonellosis cases (+ 19% annually) and a shift towards adulthood in the age groups at highest risk, while the proportion of reptile-associated salmonellosis cases among those up to four years-old decreased by 4% annually and the proportion of cases aged 45 to 74 years increased by 20% annually. We hypothesise that these findings may be the effect of the increased number and variety of reptiles that are kept as pets, calling for further attention to the issue of safe reptile–human interaction and for reinforced hygiene recommendations for reptile owners.

Inferring source attribution from a multiyear multisource data set of Salmonella in Minnesota

Salmonella enterica is a global health concern because of its widespread association with foodborne illness. Bayesian models have been developed to attribute the burden of human salmonellosis to specific sources with the ultimate objective of prioritizing intervention strategies. Important considerations of source attribution models include the evaluation of the quality of input data, assessment of whether attribution results logically reflect the data trends and identification of patterns within the data that might explain the detailed contribution of different sources to the disease burden. Here, more than 12,000 non-typhoidal Salmonella isolates from human, bovine, porcine, chicken and turkey sources that originated in Minnesota were analysed. A modified Bayesian source attribution model (available in a dedicated R package), accounting for non-sampled sources of infection, attributed 4,672 human cases to sources assessed here. Most (60%) cases were attributed to chicken, although there was a spike in cases attributed to a non-sampled source in the second half of the study period. Molecular epidemiological analysis methods were used to supplement risk modelling, and a visual attribution application was developed to facilitate data exploration and comprehension of the large multiyear data set assessed here. A large amount of within-source diversity and low similarity between sources was observed, and visual exploration of data provided clues into variations driving the attribution modelling results. Results from this pillared approach provided first attribution estimates for Salmonella in Minnesota and offer an understanding of current data gaps as well as key pathogen population features, such as serotype frequency, similarity and diversity across the sources. Results here will be used to inform policy and management strategies ultimately intended to prevent and control Salmonella infection in the state.

Molecular Approaches to Understanding Transmission and Source Attribution in Nontyphoidal Salmonella and Their Application in Africa

Nontyphoidal Salmonella (NTS) is a frequent cause of diarrhea around the world, yet in many African countries it is more commonly associated with invasive bacterial disease. Various source attribution models have been developed that utilize microbial subtyping data to assign cases of human NTS infection to different animal populations and foods of animal origin. Advances in molecular microbial subtyping approaches, in particular whole-genome sequencing, provide higher resolution data with which to investigate these sources. In this review, we provide updates on the source attribution models developed for Salmonella, and examine the application of whole-genome sequencing data combined with evolutionary modeling to investigate the putative sources and transmission pathways of NTS, with a focus on the epidemiology of NTS in Africa. This is essential information to decide where, what, and how control strategies might be applied most effectively.

Bayesian Source Attribution of Salmonellosis in South Australia

Salmonellosis is a significant cause of foodborne gastroenteritis in Australia, and rates of illness have increased over recent years. We adopt a Bayesian source attribution model to estimate the contribution of different animal reservoirs to illness due to Salmonella spp. in South Australia between 2000 and 2010, together with 95% credible intervals (CrI). We excluded known travel associated cases and those of rare subtypes (fewer than 20 human cases or fewer than 10 isolates from included sources over the 11-year period), and the remaining 76% of cases were classified as sporadic or outbreak associated. Source-related parameters were included to allow for different handling and consumption practices. We attributed 35% (95% CrI: 20-49) of sporadic cases to chicken meat and 37% (95% CrI: 23-53) of sporadic cases to eggs. Of outbreak-related cases, 33% (95% CrI: 20-62) were attributed to chicken meat and 59% (95% CrI: 29-75) to eggs. A comparison of alternative model assumptions indicated that biases due to possible clustering of samples from sources had relatively minor effects on these estimates. Analysis of source-related parameters showed higher risk of illness from contaminated eggs than from contaminated chicken meat, suggesting that consumption and handling practices potentially play a bigger role in illness due to eggs, considering low Salmonella prevalence on eggs. Our results strengthen the evidence that eggs and chicken meat are important vehicles for salmonellosis in South Australia.

Effects of Domestic Storage and Thawing Practices on Salmonella in Poultry-Based Meat Preparations

Among consumer food handling practices, time-temperature abuse has been reported as one of the most common contributory factors in salmonellosis outbreaks where the evidence is strong. The present study performed storage tests of burgers, sausages, and kebabs and investigated (i) the effect of refrigerator temperatures (4°C versus 8 or 12°C, which were the temperatures recorded in 33 and 3%, respectively, of domestic refrigerators in Italy), with or without prior temperature abuse (25°C for 2 h, simulating transport of meats from shop to home), and (ii) the impact of the thawing method (overnight in the refrigerator at 8°C versus on the kitchen countertop at 23°C) on the presence and numbers of Salmonella bacteria. Storage tests were carried out on naturally or artificially (Salmonella enterica serovar Typhimurium at ca. 10 CFU/g) contaminated products, while freezing-thawing tests were conducted only on artificially contaminated products (Salmonella Typhimurium at ca. 10, 100, and 1,000 CFU/g). The results from the artificially contaminated products showed significant (P < 0.05) growth of Salmonella Typhimurium at 12°C (i.e., from ca. 8 most probable number [MPN]/g to > 710 MPN/g) in kebabs after 7 and 10 days but more moderate growth in sausages (i.e., from ca. 14 MPN/g to a maximum of 96 MPN/g after 9 days of storage). Storage of naturally contaminated burgers or sausages (contamination at or below 1 MPN/g) at 4, 8, or 12°C and a short time of temperature abuse (2 h at 25°C) did not facilitate an increase in the presence and numbers of Salmonella bacteria. Thawing overnight in the refrigerator led to either a moderate reduction or no change of Salmonella Typhimurium numbers in burgers, sausages, and kebabs. Overall, this study showed that domestic storage and thawing practices can affect food safety and that time-temperature abuse can cause a substantial increase of Salmonella numbers in some types of poultry-based meat preparations, highlighting that efforts for the dissemination of consumer guidelines on the correct storage and handling of meats need to be continued.

Salmonella enterica Serovar Napoli Infection in Italy from 2000 to 2013: Spatial and Spatio-Temporal Analysis of Cases Distribution and the Effect of Human and Animal Density on the Risk of Infection

BACKGROUND

Salmonella Napoli is uncommon in Europe. In Italy however, it has been growing in importance since 2000. To date, no risk factors have been identified to account for its rise. This study aims at describing the epidemiology, spatial and spatio-temporal patterns of S. Napoli in Italy from 2000 to 2013, and to explore the role of several environmental correlates, namely urbanization, altitude and number of livestock farms, on the risk of S. Napoli infection among humans.

METHOD

Data were obtained from Enter-Net Italy, a network of diagnostic laboratories. The data were aggregated at the municipality level. Descriptive epidemiology, multivariate regression models, spatial and spatio-temporal analyses were performed on the number of cases and incidence rates.

RESULTS

S. Napoli showed an expanding trend at the national level, and an increasing number of cases. Compared to the other main serovars in Italy, the risk of S. Napoli infection was higher in the age group <1 year, and lower in the other age groups. Although urbanization and the number of farms were associated with the risk of S. Napoli infection to some extent, their role in the epidemiology of the disease remains inconclusive. S. Napoli cases showed a positive global spatial autocorrelation as well as a significant spatio-temporal interaction. Twenty-four spatial and spatio-temporal clusters were identified, seven purely spatial and 17 spatio-temporal, mainly in north-western Italy. Most of the clusters were in areas characterized by urban and industrial settlements surrounded by agricultural land and an abundance of freshwater bodies.

CONCLUSIONS

Our results point to the presence, in a number of areas in Italy, of a Salmonella of public health concern originating in the environment. This highlights the increasing relevance of environmental, non-food-related sources of human exposure to enteric pathogens.

Ascertaining the relationship between Salmonella Typhimurium and Salmonella 4,[5],12:i:- by MLVA and inferring the sources of human salmonellosis due to the two serovars in Italy

The current picture of human salmonellosis shows Salmonella Typhimurium and S. 4,[5],12:i:- as the most common serovars in Italy. The aims of this study were to investigate the genetic relationship between these serovars, as well as to test the possibility of inferring sources of human salmonellosis due to S. Typhimurium and S. 4,[5],12:i:- by using multilocus variable-number tandem repeat analysis (MLVA) subtyping data. Single isolates from 268 human sporadic cases and 325 veterinary isolates (from pig, cattle, chicken, and turkey) collected over the period 2009-2011 were typed by MLVA, and the similarities of MLVA profiles were investigated using different analytical approaches. Results showed that isolates of S. 4,[5],12:i:- were more clonal compared to S. Typhimurium and that clones of both serovars from different non-human sources were very close to those which were responsible for human infections, suggesting that source attribution by MLVA typing should be possible. However, using the Asymmetric Island Model it was not possible to obtain a confident ranking of sources responsible for human infections based on MLVA profiles. The source assignments provided by the model could have been jeopardized by the high heterogeneity found within each source and the negligible divergence between sources as well as by the limited source data available, especially for some species.

Survival of Salmonella Typhimurium in poultry-based meat preparations during grilling, frying and baking

The burden of food-borne diseases still represents a threat to public health; in 2012, the domestic setting accounted for 57.6% of strong-evidence EU food-borne Salmonella outbreaks. Next to cross-contamination, inadequate cooking procedure is considered as one of the most important factors contributing to food-borne illness. The few studies which have assessed the effect of domestic cooking on the presence and numbers of pathogens in different types of meat have shown that consumer-style cooking methods can allow bacteria to survive and that the probability of eating home-cooked poultry meat that still contains surviving bacteria after heating is higher than previously assumed. Thus, the main purpose of this study was to reproduce and assess the effect of several types of cooking treatments (according to label instructions and not following label instructions) on the presence and numbers of Salmonella Typhimurium DT 104 artificially inoculated in five types of poultry-based meat preparations (burgers, sausages, ready-to-cook-kebabs, quail roulades and extruded roulades) that are likely to be contaminated by Salmonella. Three contamination levels (10 cfu/g; 100 cfu/g and 1000 cfu/g) and three cooking techniques (grilling, frying and baking) were applied. Cooking treatments performed according to label instructions eliminated Salmonella Typhimurium (absence per 25g) for contamination levels of 10 and 100 cfu/g but not for contamination levels of 1000 cfu/g. After improper cooking, 26 out of 78 samples were Salmonella-positive, and 23 out of these 26 samples were artificially contaminated with bacterial loads between 100 and 1000 cfu/g. Nine out of 26 samples provided quantifiable results with a minimum level of 1.4MPN/g in kebabs (initial inoculum level: 100 cfu/g) after grilling and a maximum level of 170MPN/g recorded in sausages (initial inoculum level: 1000 cfu/g) after grilling. Kebabs were the most common Salmonella-positive meat product after cooking, followed by sausages, burgers and extruded roulades; in relation to the type of cooking treatment applied, Salmonella Typhimurium was detected mostly after frying. Thus, following label instructions mostly, but not always, produced safe cooked poultry-based meat preparations, while the application of inadequate cooking treatments was not able to assure complete elimination of Salmonella from the products even with a low contamination level (10cfu/g). Consequently, there is a need to develop guidelines for producers and consumers and promote a multidisciplinary educational campaign in order to provide information on safe cooking and time-temperature combinations able to maintain the organoleptic qualities of meat.

Tracing the sources of human salmonellosis: a multi-model comparison of phenotyping and genotyping methods

Salmonella source attribution is usually performed using frequency-matched models, such as the (modified) Dutch and Hald models, based on phenotyping data, i.e. serotyping, phage typing, and antimicrobial resistance profiling. However, for practical and economic reasons, genotyping methods such as Multi-locus Variable Number of Tandem Repeats Analysis (MLVA) are gradually replacing traditional phenotyping of salmonellas beyond the serovar level. As MLVA-based source attribution of human salmonellosis using frequency-matched models is problematic due to the high variability of the genetic targets investigated, other models need to be explored. Using a comprehensive data set from the Netherlands in 2005-2013, this study aimed at attributing sporadic and domestic cases of Salmonella Typhimurium/4,[5],12:i:- and Salmonella Enteritidis to four putative food-producing animal sources (pigs, cattle, broilers, and layers/eggs) using the modified Dutch and Hald models (based on sero/phage typing data) in comparison with a widely applied population genetics model - the asymmetric island model (AIM) - supplied with MLVA data. This allowed us to compare model outcomes and to corroborate whether MLVA-based Salmonella source attribution using the AIM is able to provide sound, comparable results. All three models provided very similar results, confirming once more that most S. Typhimurium/4,[5],12:i:- and S. Enteritidis cases are attributable to pigs and layers/eggs, respectively. We concluded that MLVA-based source attribution using the AIM is a feasible option, at least for S. Typhimurium/4,[5],12:i:- and S. Enteritidis. Enough information seems to be contained in the MLVA profiles to trace the sources of human salmonellosis even in presence of imperfect temporal overlap between human and source isolates. Besides Salmonella, the AIM might also be applicable to other pathogens that do not always comply to clonal models. This would add further value to current surveillance activities by performing source attribution using genotyping data that are being collected in a standardized fashion internationally.

Salmonella source attribution based on microbial subtyping: does including data on food consumption matter?

Source attribution based on microbial subtyping is being performed in many countries to ascertain the main reservoirs of human salmonellosis and to assess the impact of food safety interventions. To account for differences in exposure, the amount of food available for consumption within a country is often included in Salmonella source attribution models along with the level of contamination. However, not all foods have an equal probability of serving as vehicles for salmonellas, as some foods are more likely to be consumed raw/undercooked than others, posing a relatively higher risk. Using Salmonella data from the Netherlands in 2001-2004, this study aims at elucidating whether and how the incorporation of food consumption data in two source attribution models - the (modified) Dutch and Hald models - affects their attributions. We also propose the incorporation of an additional parameter to weight the amount of food consumed by its likelihood to be consumed raw/undercooked by the population. Incorporating the amount of food consumed caused a drastic change in the ranking of the top reservoirs in the Dutch model, but not in the Hald model, which proved to be insensitive to additional weightings given that its source-dependent factor can account for both food consumption and the ability for foods to serve as vehicles for salmonellas. Compared to attributions without food consumption, the Dutch model including the amount of food consumed showed an increase in the percentage of cases attributable to pigs and a decrease in that of layers/eggs, which became the second reservoir, after pigs. This was not consistent with established knowledge indicating that layers/eggs, rather than pigs, were the main reservoir of human salmonellosis in that period. By incorporating the additional weight reflecting the likelihood for different foods to be consumed raw/undercooked, the attributions of the Dutch model were effectively adjusted, both in terms of ranking and percent contributions of the different reservoirs. We concluded that incorporating food consumption data in the Dutch model can significantly affect the results. Therefore, such data should be either excluded from this model or used together with an additional weight able to adjust the amount of food consumed by its likelihood to be consumed insufficiently cooked. This may help identifying the correct reservoirs, allowing attributions to more closely reflect the real chance for a given food to serve as a vehicle for salmonellas. Conversely, the Hald model works properly irrespective of inclusion of food consumption data.

Risk factors for human salmonellosis originating from pigs, cattle, broiler chickens and egg laying hens: a combined case-control and source attribution analysis

Several case-control studies have investigated risk factors for human salmonellosis while others have used Salmonella subtyping to attribute human infections to different food and animal reservoirs. This study combined case-control and source attribution data into a single analysis to explore risk factors at the point of exposure for human salmonellosis originating from four putative food-producing animal reservoirs (pigs, cattle, broilers and layers/eggs) in the Netherlands. We confirmed that most human cases (∼90%) were attributable to layers/eggs and pigs. Layers/eggs and broilers were the most likely reservoirs of salmonellosis in adults, in urban areas, and in spring/summer, whereas pigs and cattle were the most likely reservoirs of salmonellosis in children, in rural areas, and in autumn/winter. Several reservoir-specific risk factors were identified. Not using a chopping board for raw meat only and consuming raw/undercooked meat were risk factors for infection with salmonellas originating from pigs, cattle and broilers. Consuming raw/undercooked eggs and by-products were risk factors for layer/egg-associated salmonellosis. Using antibiotics was a risk factor for pig- and cattle-associated salmonellosis and using proton-pump inhibitors for salmonellosis attributable to any reservoir. Pig- and cattle-associated infections were also linked to direct contact with animals and environmental exposure (e.g. playing in sandboxes). Eating fish, meat in pastry, and several non-meat foods (fruit, vegetables and pasteurized dairy products) were protective factors. Consuming pork and occupational exposure to animals and/or raw meats were protective against layer/egg-associated salmonellosis. We concluded that individuals acquiring salmonellosis from different reservoirs have different associated risk factors, suggesting that salmonellas may infect humans through various transmission pathways depending on their original reservoirs. The outcome of classical case-control studies can be enhanced by incorporating source attribution data and vice versa.