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There is an ongoing debate on airborne transmission of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a risk factor for infection. In this study, the level of SARS-CoV-2 in air and on surfaces of SARS-CoV-2 infected nursing home residents was assessed to gain insight in potential transmission routes. During outbreaks, air samples were collected using three different active and one passive air sampling technique in rooms of infected patients. Oropharyngeal swabs (OPS) of the residents and dry surface swabs were collected. Additionally, longitudinal passive air samples were collected during a period of 4 months in common areas of the wards. Presence of SARS-CoV-2 RNA was determined using RT-qPCR, targeting the RdRp- and E-genes. OPS, samples of two active air samplers and surface swabs with Ct-value ≤35 were tested for the presence of infectious virus by cell culture. In total, 360 air and 319 surface samples from patient rooms and common areas were collected. In rooms of 10 residents with detected SARS-CoV-2 RNA in OPS, SARS-CoV-2 RNA was detected in 93 of 184 collected environmental samples (50.5%) (lowest Ct 29.5), substantially more than in the rooms of residents with negative OPS on the day of environmental sampling (n = 2) (3.6%). SARS-CoV-2 RNA was most frequently present in the larger particle size fractions [>4 μm 60% (6/10); 1-4 μm 50% (5/10)

BACKGROUND/AIM: Active mobility may play a relevant role in the assessment of environmental exposures (e.g. traffic-related air pollution, livestock emissions), but data about actual mobility patterns are work intensive to collect, especially in large study populations, therefore estimation methods for active mobility may be relevant for exposure assessment in different types of studies. We previously collected mobility patterns in a group of 941 participants in a rural setting in the Netherlands, using week-long GPS tracking. We had information regarding personal characteristics, self-reported data regarding weekly mobility patterns and spatial characteristics. The goal of this study was to develop versatile estimates of active mobility, test their accuracy using GPS measurements and explore the implications for exposure assessment studies. METHODS: We estimated hours/week spent on active mobility based on personal characteristics (e.g. age, sex, pre-existing conditions), self-reported data (e.g. hours spent commuting per bike) or spatial predictors such as home and work address. Estimated hours/week spent on active mobility were compared with GPS measured hours/week, using linear regression and kappa statistics. RESULTS: Estimated and measured hours/week spent on active mobility had low correspondence, even the best predicting estimation method based on self-reported data, resulted in a R2 of 0.09 and Cohen's kappa of 0.07. A visual check indicated that, although predicted routes to work appeared to match GPS measured tracks, only a small proportion of active mobility was captured in this way, thus resulting in a low validity of overall predicted active mobility. CONCLUSIONS: We were unable to develop a method that could accurately estimate active mobility, the best performing method was based on detailed self-reported information but still resulted in low correspondence. For future studies aiming to evaluate the contribution of home-work traffic to exposure, applying spatial predictors may be appropriate. Measurements still represent the best possible tool to evaluate mobility patterns.

The emergence of antimicrobial resistance (AMR) is one of the biggest health threats globally. In addition, the use of antimicrobial drugs in humans and livestock is considered an important driver of antimicrobial resistance. The commensal microbiota, and especially the intestinal microbiota, has been shown to have an important role in the emergence of AMR. Mobile genetic elements (MGEs) also play a central role in facilitating the acquisition and spread of AMR genes. We isolated Escherichia coli (n = 627) from fecal samples in respectively 25 poultry, 28 swine, and 15 veal calf herds from 6 European countries to investigate the phylogeny of E. coli at country, animal host and farm levels. Furthermore, we examine the evolution of AMR in E. coli genomes including an association with virulence genes, plasmids and MGEs. We compared the abundance metrics retrieved from metagenomic sequencing and whole genome sequenced of E. coli isolates from the same fecal samples and farms. The E. coli isolates in this study indicated no clonality or clustering based on country of origin and genetic markers; AMR, and MGEs. Nonetheless, mobile genetic elements play a role in the acquisition of AMR and virulence genes. Additionally, an abundance of AMR was agreeable between metagenomic and whole genome sequencing analysis for several AMR classes in poultry fecal samples suggesting that metagenomics could be used as an indicator for surveillance of AMR in E. coli isolates and vice versa.

Any antimicrobial use (AMU) in humans and animals selects for antimicrobial resistance (AMR) and responsible AMU should therefore be promoted both in human and veterinary medicine. Insight into current AMU in companion animal clinics is necessary to be able to optimise antimicrobial (AM) prescribing behaviour. The objective of this study was to describe systemic AMU in 44 Dutch companion animal clinics over a 3-year time period (2012-2015), using retrospectively collected data. The number of Defined Daily Doses for Animals (DDDAs) per month and per clinic were calculated from prescription data for total, 1st, 2nd and 3rd choice AMU (classification according to Dutch policy on veterinary AMU). Time trends, seasonality and the influence of potential determinants (e.g., the number of dogs, cats and rabbits per clinic and other clinic characteristics) were explored using statistical modelling. Overall, the findings show that total AMU decreased over time and a shift in used classes of antimicrobials towards more 1st choice AMs was visible. Mean total AMU decreased from 1.82 DDDA/year in 2012-2013 to 1.56 DDDA/year in 2014-2015. Aminopenicillins, with and without clavulanic acid, accounted for the largest group of antimicrobials used; 38.7% (2012-2013), 40.2% (2013-2014) and 39.3% (2014-2015) of total AMU, respectively. Strong seasonal differences in AMU were found, with highest AMU in July-August and lowest in February-March. The distribution of different animal species per clinic appeared to affect AMU as well. In clinics with a larger proportion of dogs, 2nd choice AMU was significantly higher, whereas in clinics with a larger proportion of rabbits, 2nd choice AMU was significantly lower. Despite the decrease of AMU during the study period, there is still room for improvement left, especially with regard to the antimicrobial classes prescribed. According to Dutch classification of veterinary AMU, 1st choice AMs should be used as empirical therapy. A decrease in 2nd (might select for ESBL-producing bacteria) and 3rd choice AMU (i.e. fluoroquinolones and 3rd generation cephalosporins) should be aimed for.

From 2007 through 2010, the Netherlands experienced the largest Q fever epidemic ever reported. This study integrates the outcomes of a multidisciplinary research programme on spatial airborne transmission of Coxiella burnetii and reflects these outcomes in relation to other scientific Q fever studies worldwide. We have identified lessons learned and remaining knowledge gaps. This synthesis was structured according to the four steps of quantitative microbial risk assessment (QMRA): (a) Rapid source identification was improved by newly developed techniques using mathematical disease modelling; (b) source characterization efforts improved knowledge but did not provide accurate C. burnetii emission patterns; (c) ambient air sampling, dispersion and spatial modelling promoted exposure assessment; and (d) risk characterization was enabled by applying refined dose–response analyses. The results may support proper and timely risk assessment and risk management during future outbreaks, provided that accurate and structured data are available and exchanged readily between responsible actors.

Objectives The occurrence and zoonotic potential of antimicrobial resistance (AMR) in pigs and broilers has been studied intensively in past decades. Here, we describe AMR levels of European pig and broiler farms and determine the potential risk factors. Methods We collected faeces from 181 pig farms and 181 broiler farms in nine European countries. Real-time quantitative PCR (qPCR) was used to quantify the relative abundance of four antimicrobial resistance genes (ARGs) [aph(3′)-III, erm(B), sul2 and tet(W)] in these faeces samples. Information on antimicrobial use (AMU) and other farm characteristics was collected through a questionnaire. A mixed model using country and farm as random effects was performed to evaluate the relationship of AMR with AMU and other farm characteristics. The correlation between individual qPCR data and previously published pooled metagenomic data was evaluated. Variance component analysis was conducted to assess the variance contribution of all factors. Results The highest abundance of ARG was for tet(W) in pig faeces and erm(B) in broiler faeces. In addition to the significant positive association between corresponding ARG and AMU levels, we also found on-farm biosecurity measures were associated with relative ARG abundance in both pigs and broilers. Between-country and between-farm variation can partially be explained by AMU. Different ARG targets may have different sample size requirements to represent the overall farm level precisely. Conclusions qPCR is an efficient tool for targeted assessment of AMR in livestock-related samples. The AMR variation between samples was mainly contributed to by between-country, between-farm and within-farm differences, and then by on-farm AMU.

Background: There is increasing evidence of associations between residential proximity to livestock farms and respiratory morbidity, but less is known about potential effects on respiratory mortality among residents. Objectives: We aimed to assess potential associations between respiratory mortality and residential proximity to (intensive) livestock farming. Methods: In DUELS, a national census-based cohort, we selected all inhabitants from rural and semi-urban areas of the Netherlands, aged ≥30 years and living at the same address for five years up to baseline (2004). We followed these ∼4 million individuals for respiratory mortality (respiratory system diseases, chronic lower respiratory diseases, pneumonia) from 2005 to 2012. We computed the average number of cattle, pigs, chicken, and mink present in 500 m, 1000 m, 1500 m and 2000 m of each individual's residence in the period 1999–2003. Analyses were conducted using Cox proportional hazards regression, adjusting for potential confounders at individual and neighbourhood level. Results: We found evidence that living up to 2000 m of pig farms was associated with respiratory mortality, namely from chronic lower respiratory diseases, with Hazard Ratios ranging from 1.06 (1.02, 1.10) in people living close to low numbers (

Background: Livestock farms are a reservoir of antimicrobial resistant bacteria from feces. Airborne dust-bound bacteria can spread across the barn and to the outdoor environment. Therefore, exposure to farm dust may be of concern for animals, farmers and neighboring residents. Although dust is a potential route of transmission, little is known about the resistome and bacterial microbiome of farm dust.Objectives: We describe the resistome and bacterial microbiome of pig and poultry farm dust and their relation with animal feces resistomes and bacterial microbiomes, and on-farm antimicrobial usage (AMU). In addition, the relation between dust and farmers' stool resistomes was explored.Methods: In the EFFORT-study, resistomes and bacterial microbiomes of indoor farm dust collected on Electrostatic Dust fall Collectors (EDCs), and animal feces of 35 conventional broiler and 44 farrow-to-finish pig farms from nine European countries were determined by shotgun metagenomic analysis. The analysis also included 79 stool samples from farmers working or living at 12 broiler and 19 pig farms and 46 human controls. Relative abundance of and variation in resistome and bacterial composition of farm dust was described and compared to animal feces and farmers' stool.Results: The farm dust resistome contained a large variety of antimicrobial resistance genes (ARGs); more than the animal fecal resistome. For both poultry and pigs, composition of dust resistomes finds (partly) its origin in animal feces as dust resistomes correlated significantly with fecal resistomes. The dust bacterial microbiome also correlated significantly with the dust resistome composition. A positive association between AMU in animals on the farm and the total abundance of the dust resistome was found. Occupational exposure to pig farm dust or animal feces may contribute to farmers' resistomes, however no major shifts in farmers resistome towards feces or dust resistomes were found in this study.Conclusion: Poultry and pig farm dust resistomes are rich and abundant and associated with the fecal resistome of the animals and the dust bacterial microbiome.

Researchers in the air pollution field are well aware that indoor and outdoor air pollution is associated with multiple disorders, including an increase in respiratory infection-related morbidity and mortality [1, 2]. Investigating associations linking air pollution and COVID-19 spread and prognosis of respiratory disease in COVID-19 patients is therefore timely, relevant and plausible. Over a ten-day period, three papers involving original research associating COVID-19 mortality and air pollution were published [3–5]. These publications attracted considerable attention from international news outlets and on social media [6, 7]. The first study was performed in the USA and associated COVID-19 mortality in 1783 counties with reported long-term (2000–2016) average air pollution levels (fine particulate matter (PM2.5) with ∼1 km2 resolution) [3]. Adjustments on the county level were made for sixteen variables, including population size, ethnicity, hospital beds, number of individuals tested for COVID-19, weather, and socioeconomic and behavioral variables such as income, obesity and smoking. The study reported that an increase of 1 μg·m−3 in PM2.5 was associated with a remarkably large 15% increase in the COVID-19 death rate. An updated analysis, adjusted for three more confounding factors; days since first COVID-19 case reported, population age distribution, and days since issuance of stay-at-home order for each state, saw the risk estimate halved to an 8% increase in death rate [8]., Present studies on the role of air pollution and COVID-19 spread and prognosis in patients do not fulfill quality criteria and are at present not sufficiently informative.

International audience; Background High antimicrobial use (AMU) and antimicrobial resistance (AMR) in veal calves remain a source of concern. As part of the EFFORT project, the association between AMU and the abundance of faecal antimicrobial resistance genes (ARGs) in veal calves in three European countries was determined. Methods In 2015, faecal samples of veal calves close to slaughter were collected from farms located in France, Germany and the Netherlands (20 farms in France, 20 farms in the Netherlands and 21 farms in Germany; 25 calves per farm). Standardized questionnaires were used to record AMU and farm characteristics. In total, 405 faecal samples were selected for DNA extraction and quantitative polymerase chain reaction to quantify the abundance (16S normalized concentration) of four ARGs [aph(3’)-III, ermB, sul2 and tetW] encoding for resistance to frequently used antimicrobials in veal calves. Multiple linear mixed models with random effects for country and farm were used to relate ARGs to AMU and farm characteristics. Results A significant positive association was found between the use of trimethoprim/sulfonamides and the concentration of sul2 in faeces from veal calves. A higher weight of calves on arrival at the farm was negatively associated with aph(3’)-III and ermB. Lower concentrations of aph(3’)-III were found at farms with non-commercial animals present. Furthermore, farms using only water for the cleaning of stables had a significantly lower abundance of faecal ermB and tetW compared with other farms. Conclusion A positive association was found between the use of trimethoprim/sulfonamides and the abundance of sul2 in faeces in veal calves. Additionally, other relevant risk factors associated with ARGs in veal calves were identified, such as weight on arrival at the farm and cleaning practices.