The issue of antimicrobial resistance is of global concern across human and animal health. In 2016, the UK government committed to new targets for reducing antimicrobial use (AMU) in livestock. Although a number of metrics for quantifying AMU are defined in the literature, all give slightly different interpretations. This paper evaluates a selection of metrics for AMU in the dairy industry: total mg, total mg/kg, daily dose and daily course metrics. Although the focus is on their application to the dairy industry, the metrics and issues discussed are relevant across livestock sectors. In order to be used widely, a metric should be understandable and relevant to the veterinarians and farmers who are prescribing and using antimicrobials. This means that clear methods, assumptions (and possible biases), standardised values and exceptions should be published for all metrics. Particularly relevant are assumptions around the number and weight of cattle at risk of treatment and definitions of dose rates and course lengths; incorrect assumptions can mean metrics over-represent or under-represent AMU. The authors recommend that the UK dairy industry work towards the UK-specific metrics using the UK-specific medicine dose and course regimens as well as cattle weights in order to monitor trends nationally.

Our findings demonstrate that the PHQ-9, when scaled with Rasch analysis, forms a linear interval measurement of depressive symptoms suitable for use in a vision impaired population.

No abstract

No abstract

Little is known about the drivers of critically important antibacterial resistance in species with zoonotic potential present on farms (e.g. CTX-M β-lactamase-positive Escherichia coli). We collected samples – monthly, between January 2017 and December 2018 - on 53 dairy farms in South West England along with data for 610 variables concerning antibacterial usage, management practices and meteorological factors. We detected E. coli resistant to amoxicillin, ciprofloxacin, streptomycin and tetracycline, respectively, in 2754/4145 (66%), 263/4145 (6%), 1475/4145 (36%) and 2874/4145 (69%) of all samples from faecally contaminated on-farm and near-farm sites. E. coli positive for blaCTX-M were detected in 224/4145 (5.4%) of samples. Multilevel, multivariable logistic regression showed antibacterial dry cow therapeutic choice (including use of cefquinome or framycetin) to be associated with higher odds of blaCTX-M positivity. Low average monthly ambient temperature was associated with lower odds of blaCTX-M E. coli positivity in samples and with lower odds of finding E. coli resistant to each of the four test antibacterials. This was additional to the effect of temperature on total E. coli density. Furthermore, samples collected close to calves had higher odds of having E. coli resistant to each antibacterial as well as positive for blaCTX-M. Samples collected on pastureland had lower odds of having E. coli resistant to amoxicillin or tetracycline as well as lower odds of being positive for blaCTX-M. Importance Antibacterial resistance poses a significant threat to human and animal health and global food security. Surveillance for resistance on farms is important for many reasons, including to track the impacts of interventions aimed at reducing the prevalence of resistance. In this longitudinal survey of dairy farm antibacterial resistance, we showed that local temperature - as it changes over the course of a year - was associated with the prevalence of antibacterial-resistant E. coli. We also showed that prevalence of resistant E. coli was lower on pastureland and higher in environments inhabited by young animals. These findings have profound implications for routine surveillance and for surveys carried out for research. They provide important evidence that sampling at a single time-point and/or single location on a farm is unlikely to be adequate to accurately determine the status of the farm regarding the presence of samples containing resistant E. coli.