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Adverse health effects have been linked with exposure to livestock farms, likely due to airborne microbial agents. Accurate exposure assessment is crucial in epidemiological studies, however limited studies have modelled bioaerosols. This study used measured concentrations in air of livestock commensals (Escherichia coli (E. coli) and Staphylococcus species (spp.)), and antimicrobial resistance genes (tetW and mecA) at 61 residential sites in a livestock-dense region in the Netherlands. For each microbial agent, land use regression (LUR) and random forest (RF) models were developed using Geographic Information System (GIS)-derived livestock-related characteristics as predictors. The mean and standard deviation of annual average concentrations (gene copies/m3) of E. coli, Staphylococcus spp., tetW and mecA were as follows: 38.9 (±1.98), 2574 (±3.29), 20991 (±2.11), and 15.9 (±2.58). Validated through 10-fold cross-validation (CV), the models moderately explained spatial variation of all microbial agents. The best performing model per agent explained respectively 38.4%, 20.9%, 33.3% and 27.4% of the spatial variation of E. coli, Staphylococcus spp., tetW and mecA. RF models had somewhat better performance than LUR models. Livestock predictors related to poultry and pig farms dominated all models. To conclude, the models developed enable enhanced estimates of airborne livestock-related microbial exposure in future epidemiological studies. Consequently, this will provide valuable insights into the public health implications of exposure to specific microbial agents.

Metagenomic sequencing has proven to be a powerful tool in the monitoring of antimicrobial resistance (AMR). Here, we provide a comparative analysis of the resistome from pigs, poultry, veal calves, turkey, and rainbow trout, for a total of 538 herds across nine European countries. We calculated the effects of per-farm management practices and antimicrobial usage (AMU) on the resistome in pigs, broilers, and veal calves. We also provide an in-depth study of the associations between bacterial diversity, resistome diversity, and AMR abundances as well as co-occurrence analysis of bacterial taxa and antimicrobial resistance genes (ARGs) and the universality of the latter. The resistomes of veal calves and pigs clustered together, as did those of avian origin, while the rainbow trout resistome was different. Moreover, we identified clear core resistomes for each specific food-producing animal species. We identified positive associations between bacterial alpha diversity and both resistome alpha diversity and abundance. Network analyses revealed very few taxa-ARG associations in pigs but a large number for the avian species. Using updated reference databases and optimized bioinformatics, previously reported significant associations between AMU, biosecurity, and AMR in pig and poultry farms were validated. AMU is an important driver for AMR; however, our integrated analyses suggest that factors contributing to increased bacterial diversity might also be associated with higher AMR load. We also found that dispersal limitations of ARGs are shaping livestock resistomes, and future efforts to fight AMR should continue to emphasize biosecurity measures.IMPORTANCEUnderstanding the occurrence, diversity, and drivers for antimicrobial resistance (AMR) is important to focus future control efforts. So far, almost all attempts to limit AMR in livestock have addressed antimicrobial consumption. We here performed an integrated analysis of the resistomes of five important farmed ani

Comparisons between antimicrobial usage (AMU) in humans and food-producing animals are regularly made. The accuracy of such comparisons depends on the indicators used to quantify AMU. Indicators for AMU quantitatively relate use data (the numerator) to population data (the denominator). The denominator should be a proxy for the population at risk in a certain period when comparing the exposure of different populations to antimicrobials. Denominators based on numbers of animals slaughtered, such as the commonly used population correction unit, do not consider the time at risk of antimicrobial treatment. Production-based indicators underestimate animal AMU. Additionally, production-based indicators are fundamentally different from indicators used to quantify human AMU. Using such indicators to compare human and animal AMU therefore leads to biased results. More caution should be taken in selecting the indicator to quantify AMU when comparing AMU in food-producing animals and humans.

BACKGROUND: Evidence on the public health relevance of exposure to livestock farm emissions is increasing. Research mostly focused on chemical air pollution, less on microbial exposure, while endotoxins are suggested relevant bacterial components in farm emissions. Acute respiratory health effects of short-term exposure to livestock-related air pollution has been shown for NH 3 and PM 10, but has not yet been studied for endotoxin. We aimed to assess associations between lung function and short-term exposure to livestock farming emitted endotoxin in co-pollutant models with NH 3 and PM 10. METHODS: In 2014/2015, spirometry was conducted in 2308 non-farming residents living in a rural area in the Netherlands. Residential exposure to livestock farming emitted endotoxin during the week prior to spirometry was estimated by dispersion modelling. The model was applied to geo-located individual barns within 10 km of each home address using provincial farm data and local hourly meteorological conditions. Regional week-average measured concentrations of NH 3 and PM 10 were obtained through monitoring stations. Lung function parameters (FEV 1, FVC, FEV 1/FVC, MMEF) were expressed in %-predicted value based on GLI-2012. Exposure-response analyses were performed by linear regression modelling. RESULTS: Week-average endotoxin exposure was negatively associated with FVC, independently from regional NH 3 and PM 10 exposure. A 1.1% decline in FVC was estimated for an increase of endotoxin exposure from 10th to 90th percentile. Stratified analyses showed a larger decline (3.2%) for participants with current asthma and/or COPD. FEV 1 was negatively associated with week-average endotoxin exposure, but less consistent after co-pollutant adjustment. FEV 1/FVC and MMEF were not associated with week-average endotoxin exposure. CONCLUSIONS: Lower lung function in non-farming residents was observed in relation to short-term residential exposure to livestock farming e

The use of antimicrobials in the pig sector in the Netherlands has been reduced by more than 70% over the last decade. However, there is still a considerable number of pig farms that have not been able to lower their antimicrobial usage (AMU) to a sufficiently low level, comparable to the majority of the other pig farms. Therefore, an intervention study was initiated to lower on-farm antimicrobial use in which 45 pig farms with high AMU were recruited. These farms were coached over a period of 2 years whereby different management interventions were introduced. During the 2-year study period a significant reduction of 13 and 17% in total AMU was seen in weaned piglets and fattening pigs respectively. The introduction of coaching as well as multiple management interventions were (univariably) associated with the decrease in AMU. After mutual adjustment of coaching and individual interventions, the association between coaching and AMU became substantially weaker, indicating that coaching and interventions were interrelated and specific interventions explained the reduction in AMU. In conclusion, a coaching effect was observed in this study, with an effect on AMU through specific interventions. More insights are needed regarding the role and effects of coaching on the influence on the management team comprising the farmer, veterinarian and (feed) advisor, and interventions implemented.

Due to globally increasing antimicrobial resistance (AMR), it is pivotal to understand factors contributing to antimicrobial use (AMU) to enable development and implementation of AMR-reducing interventions. Therefore, we explored seasonal variations of systemic AMU in food-producing animals in the Netherlands. Dutch surveillance data from January 2013 to December 2018 from cattle, pig, and broiler farms were used. AMU was expressed as the number of Defined Daily Dosages Animal per month (DDDA/animal-month) per farm by animal sector, antimicrobial line (first, second, and third), antimicrobial class, and farm type. Seasonality of AMU was analyzed using Generalized Additive Models (GAMs) with DDDA/animal-month as outcome variable, and year and month as independent variables. Year and month were modelled as smooth terms represented with penalized regression splines.Significant seasonality of AMU was found in the cattle and pig sectors, but not in broilers. Significant seasonality of AMU was found mainly for first-line antimicrobials. In the cattle sector, a significant increase during winter was found for the use of amphenicols (an increase of 23.8%) and long-acting macrolides (an increase of 3.4%). In the pig sector, seasonality of AMU was found for pleuromutilins (p < 0.001) with an increase of 20% in October-November. The seasonality of pleuromutilins was stronger in sows/piglets (an increase of 47%) than in fattening pigs (16% increase). Only in fattening pigs, the use of amphenicols showed a significant seasonality with an increase of 11% during winter (P < 0.001). AMU in cattle and pig sectors shows seasonal variations likely caused by seasonality of diseases. In broilers, no AMU seasonality was observed, possibly due to the controlled environment in Dutch farms. In the context of the one health concept, future studies are necessary to explore whether this seasonality is present in other populations and whether it has implications for antimicrobial resis

BACKGROUND: Previous studies, performed between 2009-2019, in the Netherlands observed an until now still unexplained increased risk for pneumonia among residents living close to goat farms. Since data were collected in the provinces Noord-Brabant and Limburg (NB-L), an area with relatively high air pollution levels and proximity to large industrial areas in Europe, the question remains whether the results are generalizable to other regions. In this study, a different region, covering the provinces Utrecht, Gelderland, and Overijssel (UGO) with a similar density of goat farms, was included to assess whether the association between goat farm proximity and pneumonia is consistently observed across the Netherlands.METHODS: Data for this study were derived from the Electronic Health Records (EHR) of 21 rural general practices (GPs) in UGO, for 2014-2017. Multi-level analyses were used to compare annual pneumonia prevalence between UGO and data derived from rural reference practices ('control area'). Random-effects meta-analysis (per GP practice) and kernel analyses were performed to study associations of pneumonia with the distance between goat farms and patients' home addresses.RESULTS: GP diagnoses of pneumonia occurred 40% more often in UGO compared to the control area. Meta-analysis showed an association at a distance of less than 500m (~70% more pneumonia compared to >500m) and 1000m (~20% more pneumonia compared to >1000m). The kernel-analysis for three of the four individual years showed an increased risk up to a distance of one or two kilometers (2-36% more pneumonia; 10-50 avoidable cases per 100,000 inhabitants per year).CONCLUSIONS: The positive association between living in the proximity of goat farms and pneumonia in UGO is similar to the previously found association in NB-L. Therefore, we concluded that the observed associations are relevant for regions with goat farms in the entire country.

The emergence of bacterial strains resistant to topical antimicrobials in both human and veterinary medicine has raised concerns over retaining the efficacy of these preparations. Yet, little information is available regarding the use of topical antimicrobials in either sector for planning targeted interventions. This study aims to quantify the use of topical antimicrobials in 44 Dutch companion animal clinics before and during their participation in an antimicrobial stewardship programme (ASP), to explore the effect of the intervention on topical antimicrobial use (AMU). Hence, prescription and clinic animal population data, collected from July 2012 until June 2018 were used. Specifically, the period from July 2012 until June 2015 was defined as pre-intervention period, whereas clinics started to participate in the ASP from March 2016 onwards. As quantification metric, the Defined Daily Dose for Animals (DDDA) was used and a mixed effect times series model with auto-regression was applied to monthly topical AMU data. The intervention effect was modelled using a step function with a change in (linear) time trend and clinic characteristics, as potential determinants of topical AMU, were assessed using a multivariable regression model. A seasonal pattern was identified, in the pre-intervention period, where topical AMU was highest in July-August and lowest in February-March. In addition, total topical AMU appeared to significantly decrease over time in the pre-intervention period and the proportion of dogs in the clinic was positively associated with topical AMU. The intervention effect was significant only for second line and for skin product AMU. This study demonstrates that during participation in an ASP, second line and skin product AMU decreased in Dutch companion animal clinics. Additionally, this study demonstrates the existence of a seasonal effect and a decrease in topical AMU over time already before introduction of a targeted intervention.

BACKGROUND: Occupational exposure to soluble chlorinated platinum (Pt) salts, commonly called chloroplatinates, is a known cause of Pt salt sensitisation (PSS) and occupational asthma. We aimed to model inhalable soluble Pt salts exposure levels based on measurements in precious metal refineries for use in a retrospective cohort study on PSS.METHODS: Five platinum refineries located in the United Kingdom (3 sites), United States, and South Africa provided time weighted average inhalable soluble Pt salts exposure data, measured in 2,982 personal air samples over a 17-year period (2000-2016). We used a Bayesian hierarchical model to estimate geometric mean (GM) exposure levels for each refinery and job title over time.RESULTS: The GM of measured exposure levels over all facilities was 92 ng/m3 with a geometric standard deviation (GSD) of 9.07. Facility-specific GMs ranged from 48 ng/m3 (GSD 15.3) to 242 ng/m3 (GSD 5.99). Exposure modelling showed that soluble Pt salts exposure levels declined approximately 10% per year in two of the five facilities, but there were no clear time trends in the other facilities. A priori specified exposure groups captured most of the between-jobs differences, which helps to accurately predict exposures for jobs with no measurement data available.CONCLUSIONS: We applied exposure modelling to estimate time, refinery, and job-specific soluble Pt salts exposures. A significant annual decline in exposure levels was observed in two of the five participating facilities. Modelled exposure levels can be linked to individual workers' job history for exposure-response analysis of PSS in an epidemiological study.