Abstract
Objectives
Farm-level quantification of antimicrobial usage (AMU) in pig farms.
Methods
In a cross-sectional study, AMU data on group treatments administered to a single batch of fattening ...pigs from birth to slaughter (group treatment data) and antimicrobials purchased during 1 year (purchase data) were collected at 180 pig farms in nine European countries. AMU was quantified using treatment incidence (TI) based on defined (DDDvet) and used (UDDvet) daily doses and defined (DCDvet) and used (UCDvet) course doses.
Results
The majority of antimicrobial group treatments were administered to weaners (69.5% of total TIDDDvet) followed by sucklers (22.5% of total TIDDDvet). AMU varied considerably between farms with a median TIDDDvet of 9.2 and 7.1 for a standardized rearing period of 200 days based on group treatment and purchase data, respectively. In general, UDDvet and UCDvet were higher than DDDvet and DCDvet, respectively, suggesting that either the defined doses were set too low or that group treatments were often dosed too high and/or administered for too long. Extended-spectrum penicillins (31.2%) and polymyxins (24.7%) were the active substances most often used in group treatments, with the majority administered through feed or water (82%). Higher AMU at a young age was associated with higher use in older pigs.
Conclusions
Collecting farm-level AMU data of good quality is challenging and results differ based on how data are collected (group treatment data versus purchase data) and reported (defined versus used daily and course doses).
Animal rehabilitation centres provide a unique opportunity to study the microbiome of wild animals because subjects will be handled for their treatment and can therefore be sampled longitudinally. ...However, rehabilitation may have unintended consequences on the animals' microbiome because of a less varied and suboptimal diet, possible medical treatment and exposure to a different environment and human handlers. Our study describes the gut microbiome of two large seal cohorts, 50 pups (0-30 days old at arrival) and 23 weaners (more than 60 days old at arrival) of stranded harbour seals admitted for rehabilitation at the Sealcentre Pieterburen in the Netherlands, and the effect of rehabilitation on it. Faecal samples were collected from all seals at arrival, two times during rehabilitation and before release. Only seals that did not receive antimicrobial treatment were included in the study. The average time in rehabilitation was 95 days for the pups and 63 days for the weaners. We observed that during rehabilitation, there was an increase in the relative abundance of some of the Campylobacterota spp and Actinobacteriota spp. The alpha diversity of the pups' microbiome increased significantly during their rehabilitation (p-value <0.05), while there were no significant changes in alpha diversity over time for weaners. We hypothesize that aging is the main reason for the observed changes in the pups' microbiome. At release, the sex of a seal pup was significantly associated with the microbiome's alpha (i.e., Shannon diversity was higher for male pups, p-value <0.001) and beta diversity (p-value 0.001). For weaners, variation in the microbiome composition (beta diversity) at release was partly explained by sex and age of the seal (p-values 0.002 and 0.003 respectively). We mainly observed variables known to change the gut microbiome composition (e.g., age and sex) and conclude that rehabilitation in itself had only minor effects on the gut microbiome of seal pups and seal weaners.
Abstract
Objectives
To control the emerging threat of antimicrobial resistance, international policy appeals for appropriate monitoring of antimicrobial usage (AMU) at supranational, species and farm ...level. The aim of this study was to quantify AMU in broilers at farm and flock level in nine European countries.
Methods
Antimicrobial treatment data of one flock and purchased antimicrobials over one year were collected at 181 European broiler farms. Afterwards AMU was quantified using treatment incidence (TI) per 100 days based on Defined Daily Dose (DDDvet), Defined Course Dose (DCDvet) or Used Daily Dose (UDDvet) values. Total AMU at flock level was obtained by summing the TIDDDvet of all treatments in the sampled flock (TIDDDvetFl*).
Results
The median TIDDDvetFl* was 9.0 (95% CI 5.5–10.8), meaning that broilers were treated with antimicrobials during 9% of their rearing period. TIDDDvetFl* varied considerably within and between countries. However, in every country at least one untreated flock was present. Average TIDDDvetFl* at country level ranged from 3.3 to 36.7. Polymyxins, extended-spectrum aminopenicillins and fluoroquinolones were the most used antimicrobials, accounting for 26%, 26% and 18% of total AMU, respectively. Twenty-six percent of the farms started a treatment on day 1 of production, and 49% of overall AMU was administered within the first week.
Conclusions
Results show that rearing broilers without AMU is feasible. However, a huge variation in AMU in terms of amount, moment of administration and antimicrobial classes was observed. This shows that there is still ground to be covered when it comes to AMU on broiler farms.
•An analysis of farm dust from 79 pig and poultry farms using metagenomics.•Farm dust resistomes are compared to animal fecal and farmers stool resistomes.•Farm dust bacterial microbiomes have an ...abundant and rich resistome.•Animal fecal resistomes are an important, but not the only, source of farm dust ARGs.•Antimicrobial usage in the animals is positively associated with dust resistome abundance.
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.
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.
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.
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.
Poultry and pig farm dust resistomes are rich and abundant and associated with the fecal resistome of the animals and the dust bacterial microbiome.
Several studies have described the faecal microbiota of horses and the factors that influence its composition, but the variation in results is substantial. This study aimed to investigate the ...microbiota composition in healthy equids in The Netherlands under standard housing and management conditions and to evaluate the effect of age, gender, horse type, diet, pasture access, the season of sampling and location on it. Spontaneously produced faecal samples were collected from the stall floor of 79 healthy horses and ponies at two farms. The validity of this sampling technique was evaluated in a small pilot study including five ponies showing that the microbiota composition of faecal samples collected up to 6 h after spontaneous defaecation was similar to that of the samples collected rectally. After DNA extraction, Illumina Miseq 16S rRNA sequencing was performed to determine microbiota composition. The effect of host and environmental factors on microbiota composition were determined using several techniques (NMDS, PERMANOVA, DESeq2). Bacteroidetes was the largest phylum found in the faecal microbiota (50.1%), followed by Firmicutes (28.4%). Alpha-diversity and richness decreased significantly with increasing age. Location, age, season, horse type and pasture access had a significant effect on beta-diversity. The current study provides important baseline information on variation in faecal microbiota in healthy horses and ponies under standard housing and management conditions. These results indicate that faecal microbiota composition is affected by several horse-related and environment-related factors, and these factors should be considered in future studies of the equine faecal microbiota.
Antimicrobial use (AMU) has decreased significantly in Dutch pig farms since 2009. However, this decrease has stagnated recently, with relatively high AMU levels persisting mainly among weaners. The ...aim of this study was to identify farm-level characteristics associated with: i) total AMU and ii) use of specific antimicrobial classes.
In 2020, cross-sectional data from 154 Dutch pig farms were collected, including information on AMU and farm characteristics. A mixed-effects conditional Random Forest analysis was applied to select the subset of features that was best associated with AMU.
The main risk factors for total AMU in weaners were vaccination for PRRS in sucklings, being a conventional farm (vs. not), high within-farm density, and early weaning. The main protective factors for total AMU in sows/sucklings were E. coli vaccination in sows and having boars for estrus detection from own production. Regarding antimicrobial class-specific outcomes, several risk factors overlapped for weaners and sows/sucklings, such as farmer's non-tertiary education, not having free-sow systems during lactation, and conventional farming. An additional risk factor for weaners was having fully slatted floors. For fatteners, the main risk factor for total AMU was PRRS vaccination in sucklings.
Several factors found here to be associated with AMU. Some were known but others were novel, such as farmer's tertiary education, low pig aggression and free-sow systems which were all associated with lower AMU. These factors provide targets for developing tailor-made interventions, as well as an evidence-based selection of features for further causal assessment and mediation analysis.
•Tetracyclines and penicillins are the most used antimicrobial classes in Dutch pig farms.•PRRS vaccination in sucklings displays the largest risk effect size for total antimicrobial use in weaners and fatteners.•Late weaning is consistently associated with lower antimicrobial use in both weaners and sows/sucklings.
Abstract
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.
Abstract
Objectives
Previous studies in food-producing animals have shown associations between antimicrobial use (AMU) and resistance (AMR) in specifically isolated bacterial species. Multi-country ...data are scarce and only describe between-country differences. Here we investigate associations between the pig faecal mobile resistome and characteristics at the farm-level across Europe.
Methods
A cross-sectional study was conducted among 176 conventional pig farms from nine European countries. Twenty-five faecal samples from fattening pigs were pooled per farm and acquired resistomes were determined using shotgun metagenomics and the Resfinder reference database, i.e. the full collection of horizontally acquired AMR genes (ARGs). Normalized fragments resistance genes per kilobase reference per million bacterial fragments (FPKM) were calculated. Specific farm-level data (AMU, biosecurity) were collected. Random-effects meta-analyses were performed by country, relating farm-level data to relative ARG abundances (FPKM).
Results
Total AMU during fattening was positively associated with total ARG (total FPKM). Positive associations were particularly observed between widely used macrolides and tetracyclines, and ARGs corresponding to the respective antimicrobial classes. Significant AMU-ARG associations were not found for β-lactams and only few colistin ARGs were found, despite high use of these antimicrobial classes in younger pigs. Increased internal biosecurity was directly related to higher abundances of ARGs mainly encoding macrolide resistance. These effects of biosecurity were independent of AMU in mutually adjusted models.
Conclusions
Using resistome data in association studies is unprecedented and adds accuracy and new insights to previously observed AMU-AMR associations. Major components of the pig resistome are positively and independently associated with on-farm AMU and biosecurity conditions.
Minimizing antimicrobial use (AMU) in livestock is needed to control antimicrobial resistance (AMR). In the Netherlands, the livestock sector reduced AMU by almost 70 % since 2009, but this reduction ...stagnated in recent years. With only therapeutic AMU allowed, it is clear that besides socio-economic and behavioral factors, also the farm technical characteristics influence the conditions under which farmers need AMU. These characteristics pertain to farm management, including biosecurity, vaccination schemes, nutrition, micro-climate and husbandry practices. Identifying farm-related risk factors for AMU is needed to control AMR in a sustainable and pragmatic way. This need, often concerns the overall contribution of seemingly related (rather than individualized) factors. Here, risk factors for AMU in pig and calf farms were determined using two approaches: a typical risk factor analysis based on generalized estimating equations (GEEs) or hierarchical mixed-effects models and a multiblock partial least-squares regression analysis. These methods were applied to longitudinal data from two previous studies, i.e. a panel study and an intervention study involving 36 multiplier pig farms and 51 veal calf farms in the Netherlands, respectively. The multiblock analysis allowed us to quantify the importance of each factor and their respective block (i.e. farm management domain). For pigs, factors related to internal biosecurity had the highest impact on AMU, while for calves, these were mainly related to micro-climate. Structural characteristics, such as farm size and production type, followed in importance for both sectors. While both methods provided similar outcomes, the multiblock approach provided further insights by grouping and comparing factors believed to be inter-related.
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