Are management measures efficacious to prevent or control colibacillosis in broilers production chain? A protocol for a systematic review and meta-analysis.

The protocol is established as an essential and basic tool to perform a systematic review on the efficacy of biosecurity measures to prevent or reduce colibacillosis in broilers

Mutations potentially associated with decreased susceptibility to fluoroquinolones, macrolides and lincomycin in Mycoplasma synoviae.

Mycoplasma synoviae is one of the economically most significant avian Mycoplasma species. It can cause great financial losses to the poultry industry by inducing respiratory diseases, infectious synovitis, or eggshell apex abnormalities. There are different approaches to control M. synoviae infection. Although antimicrobial therapy cannot replace long-term solutions, like eradication and vaccination, this strategy can be effective in the short term, as adequate antibiotic treatment can relieve economic losses through the attenuation of clinical signs and reduction of transmission. Using broth microdilution method, minimal inhibitory concentration (MIC) values to fourteen antibiotics related to eight antimicrobial groups were determined in 96 M. synoviae strains. Whole genome sequencing and sequence analysis revealed mutations potentially associated with decreased susceptibility to fluoroquinolones, macrolides and lincomycin. Molecular markers responsible for the high MICs to fluoroquinolones were found in the gyrA, gyrB, parC and parE genes. Besides, single nucleotide polymorphisms identified in genes encoding the 23S rRNA were found to be responsible for high MICs to the 50S inhibitor macrolides and lincomycin, while amino acid change in the 50S ribosomal protein L22 could be associated with decreased susceptibility to macrolides. The revealed mutations can contribute to the extension of knowledge about the genetic background of antibiotic resistance in M. synoviae. Moreover, the explored potentially resistance-related mutations may serve as targets for molecular biological assays providing data of antibiotic susceptibility prior to the laborious and timeconsuming isolation of M. synoviae strains

Antimicrobial resistance monitoring in commensal and clinical Escherichia coli from broiler chickens : Differences and similarities

Background: In the Netherlands, antimicrobial resistance (AMR) is monitored in commensal indicator Escherichia coli from healthy broilers at slaughter as part of a European monitoring programme. In a separate programme for poultry health, AMR is monitored in veterinary pathogens from diseased broilers. So far, it is unknown how the outcomes of these two AMR monitoring approaches in the same animal population are associated. Aims: This study aims to investigate the association between the outcomes of monitoring non-wildtype susceptibility (using epidemiological cut-off values, ECOFF, as prescribed by EU legislation) in commensal E. coli isolated from healthy broilers (i.e. active surveillance) with the outcomes of monitoring clinical resistance (using clinical breakpoints, to determine susceptibility for antibiotic treatment in veterinary practice) in E. coli isolated from diseased broilers (i.e. passive surveillance). Methods: Data acquired by broth microdilution was analysed for commensal indicator E. coli and clinical E. coli from the Netherlands, 2014–2019. A generalized linear multivariable model (Poisson regression) was used to determine time trends and identify differences in mean resistant proportions. Results: Observed resistant proportions of the monitored commensal E. coli and clinical E. coli were similar with overlapping confidence intervals for most time points for ampicillin, gentamicin, cefotaxime, tetracycline, colistin and trimethoprim/sulfonamide. The statistical analysis showed that only for cefotaxime and tetracycline, mean resistant proportions were different. In commensal E. coli, a decrease of resistant proportions over time was observed, except for gentamicin. In clinical E. coli, no time trend was detected in resistant proportions, except for cefotaxime and colistin. Conclusions: Generally, the resistant proportions monitored in commensal and clinical E. coli were similar. However, some relevant differences were found, which can be explained by the type of monitoring approach, i.e. active or passive surveillance. The random sample of commensal E. coli isolated from healthy animals (active surveillance), was more suitable to monitor AMR time trends. The sample of clinical isolates from diseased animals (passive surveillance), resulted in a higher chance to detect low-prevalent resistance: i.e. cefotaxime and colistin. The clinical E. coli data showed more fluctuation over time, and data from a longer period of time would be needed to determine the association. This study shows the value of both an active and a passive surveillance component for AMR monitoring

Antimicrobial resistance monitoring in commensal and clinical Escherichia coli from broiler chickens: Differences and similarities

Background: In the Netherlands, antimicrobial resistance (AMR) is monitored in commensal indicator Escherichia coli from healthy broilers at slaughter as part of a European monitoring programme. In a separate programme for poultry health, AMR is monitored in veterinary pathogens from diseased broilers. So far, it is unknown how the outcomes of these two AMR monitoring approaches in the same animal population are associated. Aims: This study aims to investigate the association between the outcomes of monitoring non-wildtype susceptibility (using epidemiological cut-off values, ECOFF, as prescribed by EU legislation) in commensal E. coli isolated from healthy broilers (i.e. active surveillance) with the outcomes of monitoring clinical resistance (using clinical breakpoints, to determine susceptibility for antibiotic treatment in veterinary practice) in E. coli isolated from diseased broilers (i.e. passive surveillance). Methods: Data acquired by broth microdilution was analysed for commensal indicator E. coli and clinical E. coli from the Netherlands, 2014–2019. A generalized linear multivariable model (Poisson regression) was used to determine time trends and identify differences in mean resistant proportions. Results: Observed resistant proportions of the monitored commensal E. coli and clinical E. coli were similar with overlapping confidence intervals for most time points for ampicillin, gentamicin, cefotaxime, tetracycline, colistin and trimethoprim/sulfonamide. The statistical analysis showed that only for cefotaxime and tetracycline, mean resistant proportions were different. In commensal E. coli, a decrease of resistant proportions over time was observed, except for gentamicin. In clinical E. coli, no time trend was detected in resistant proportions, except for cefotaxime and colistin. Conclusions: Generally, the resistant proportions monitored in commensal and clinical E. coli were similar. However, some relevant differences were found, which can be explained by the type of monitoring approach, i.e. active or passive surveillance. The random sample of commensal E. coli isolated from healthy animals (active surveillance), was more suitable to monitor AMR time trends. The sample of clinical isolates from diseased animals (passive surveillance), resulted in a higher chance to detect low-prevalent resistance: i.e. cefotaxime and colistin. The clinical E. coli data showed more fluctuation over time, and data from a longer period of time would be needed to determine the association. This study shows the value of both an active and a passive surveillance component for AMR monitoring

Colistin Use in European Livestock: Veterinary Field Data on Trends and Perspectives for Further Reduction

Polymyxin E (colistin) is a medically important active substance both in human and veterinary medicine. Colistin has been used in veterinary medicine since the 1950s. Due to the discovery of the plasmid-borne mcr gene in 2015 and the simultaneously increased importance in human medicine as a last-resort antibiotic, the use of colistin for animals was scrutinised. Though veterinary colistin sales dropped by 76.5% between 2011 to 2020, few studies evaluated real-world data on the use patterns of colistin in different European countries and sectors. A survey among veterinarians revealed that 51.9% did not use or ceased colistin, 33.4% decreased their use, 10.4% stabilised their use, and 2.7% increased use. The most important indications for colistin use were gastrointestinal diseases in pigs followed by septicaemia in poultry. A total of 106 (16.0%) responding veterinarians reported governmental/industry restrictions regarding colistin use, most commonly mentioning “use only after susceptibility testing” (57%). In brief, colistin was perceived as an essential last-resort antibiotic in veterinary medicine for E. coli infections in pigs and poultry, where there is no alternative legal, safe, and efficacious antimicrobial available. To further reduce the need for colistin, synergistic preventive measures, including improved biosecurity, husbandry, and vaccinations, must be employed

Seasonal risk of low pathogenic avian influenza virus introductions into free-range layer farms in the Netherlands

Poultry can become infected with avian influenza viruses (AIV) via (in) direct contact with infected wild birds. Free-range chicken farms in the Netherlands were shown to have a higher risk for introduction of low pathogenic avian influenza (LPAI) virus than indoor chicken farms. Therefore, during outbreaks of highly pathogenic avian influenza (HPAI), free-range layers are confined indoors as a risk mitigation measure. In this study, we characterized the seasonal patterns of AIV introductions into free-range layer farms, to determine the high-risk period. Data from the LPAI serological surveillance programme for the period 2013–2016 were used to first estimate the time of virus introduction into affected farms and then assess seasonal patterns in the risk of introduction. Time of introduction was estimated by fitting a mathematical model to seroprevalence data collected longitudinally from infected farms. For the period 2015–2016, longitudinal follow-up included monthly collections of eggs for serological testing from a cohort of 261 farms. Information on the time of introduction was then used to estimate the monthly incidence and seasonality by fitting harmonic and Poisson regression models. A significant yearly seasonal risk of introduction that lasted around 4 months (November to February) was identified with the highest risk observed in January. The risk for introduction of LPAI viruses in this period was on average four times significantly higher than the period of low risk around the summer months. Although the data for HPAI infections were limited in the period 2014–2018, a similar risk period for introduction of HPAI viruses was observed. The results of this study can be used to optimize risk-based surveillance and inform decisions on timing and duration of indoor confinement when HPAI viruses are known to circulate in the wild bird population.</p

Monitoring of Antibiotic Resistance in Veterinary Pathogens by re-use of data? Validation of existing databases

The emergence and spread of antimicrobial resistance (AMR) is considered a serious public health threat with use of antimicrobials in animals considered as one of the potential risk factors. In the Netherlands, veterinary use of antimicrobials (AMU) has been decreased substantially in the last decade and gradually the focus shifts to ‘prudent use’: reduction and refinement of AMU. To achieve 'prudent use' in practice, among others evidence-based treatment guidelines are required and therefore knowledge on prevalences of AMR in animal pathogens is pivotal. One of the aims of this project was to aggregate existing antimicrobial susceptibility testing (AST) results for different animal species/bacterial species combinations in the time period from 2016 up to (including) 2020 in MIC distributions. Additional analyses, to evaluate representativeness and reliability of the aggregrated AST results are not shown here but are reported seperately in the final ZonMw report. To obtain aggregated AST results (MIC (Minimal Inhibitory Concentration) distributions) for different, pathogenic bacterial species from pigs, poultry, veal calves, dogs and cats were extracted from the Laboratory Information Management Systems (LIMS) of GD (pigs, poultry, veal calves) and of VMDC/UU (dogs and cats), from January 2016 to January 2021. Actual bacteriological culture and AST of bacterial isolates were not part of this project. Upon extraction of MIC values from the LIMS of Royal GD and VMDC/UU, datasets of MIC values were validated and next, STATA 15.1 (StataCorp, 2017) was used for calculation of MIC50 (the lowest concentration that inhibits growth of 50% of the isolates) and MIC90 (the lowest concentration that inhibits growth of 90% of the isolates) values, calculation of percentages susceptible isolates and percentages resistant isolates and the resistance confidence interval, creation of MIC distributions for each bacterial species/animal species combination. This data package contains MIC distributions over time period 2016 - 2019 and 2020 (separately) from: - Actinobacillus Pleuropneumoniae, Escherichia coli and Streptococcus suis isolates originating from pig (samples) - Escherichia coli isolates originating from poultry (broilers) - Salmonella dublin and Salmonella Typhimurium isolates originating from veal calves - Escherichia coli, Staphylococcus spp. and Pseudomonas aeruginosa isolates originating from dogs - Escherichia coli and Staphylococcus spp. originating from cat

Antimicrobial resistance monitoring in commensal and clinical Escherichia coli from broiler chickens: Differences and similarities

Background: In the Netherlands, antimicrobial resistance (AMR) is monitored in commensal indicator Escherichia coli from healthy broilers at slaughter as part of a European monitoring programme. In a separate programme for poultry health, AMR is monitored in veterinary pathogens from diseased broilers. So far, it is unknown how the outcomes of these two AMR monitoring approaches in the same animal population are associated. Aims: This study aims to investigate the association between the outcomes of monitoring non-wildtype susceptibility (using epidemiological cut-off values, ECOFF, as prescribed by EU legislation) in commensal E. coli isolated from healthy broilers (i.e. active surveillance) with the outcomes of monitoring clinical resistance (using clinical breakpoints, to determine susceptibility for antibiotic treatment in veterinary practice) in E. coli isolated from diseased broilers (i.e. passive surveillance). Methods: Data acquired by broth microdilution was analysed for commensal indicator E. coli and clinical E. coli from the Netherlands, 2014–2019. A generalized linear multivariable model (Poisson regression) was used to determine time trends and identify differences in mean resistant proportions. Results: Observed resistant proportions of the monitored commensal E. coli and clinical E. coli were similar with overlapping confidence intervals for most time points for ampicillin, gentamicin, cefotaxime, tetracycline, colistin and trimethoprim/sulfonamide. The statistical analysis showed that only for cefotaxime and tetracycline, mean resistant proportions were different. In commensal E. coli, a decrease of resistant proportions over time was observed, except for gentamicin. In clinical E. coli, no time trend was detected in resistant proportions, except for cefotaxime and colistin. Conclusions: Generally, the resistant proportions monitored in commensal and clinical E. coli were similar. However, some relevant differences were found, which can be explained by the type of monitoring approach, i.e. active or passive surveillance. The random sample of commensal E. coli isolated from healthy animals (active surveillance), was more suitable to monitor AMR time trends. The sample of clinical isolates from diseased animals (passive surveillance), resulted in a higher chance to detect low-prevalent resistance: i.e. cefotaxime and colistin. The clinical E. coli data showed more fluctuation over time, and data from a longer period of time would be needed to determine the association. This study shows the value of both an active and a passive surveillance component for AMR monitoring