Busani, L. | bioRxiv Science

bioRxiv2021

Defining the scope of the European Antimicrobial Resistance Surveillance network in Veterinary medicine (EARS-Vet): a bottom-up and One Health approach

BackgroundBuilding the European Antimicrobial Resistance Surveillance network in Veterinary medicine (EARS-Vet) was proposed to strengthen the European One Health antimicrobial resistance (AMR) surveillance approach. ObjectivesThe objectives were to (i) define the combinations of animal species, production types, age categories, bacterial species, specimens and antimicrobials to be monitored in EARS-Vet and to (ii) determine antimicrobial test panels able to cover most combinations. MethodsThe EARS-Vet scope was defined by consensus between 26 European experts. Decisions were guided by a survey of the combinations that are relevant and feasible to monitor in diseased animals in 13 European countries (bottom-up approach). Experts also considered the One Health approach and the need for EARS-Vet to complement existing European AMR monitoring systems coordinated by the European Centre for Disease Prevention and Control (ECDC) and the European Food Safety Authority (EFSA). ResultsEARS-Vet would monitor AMR in six animal species (cattle, swine, chicken (broiler and laying hen), turkey, cat and dog), for 11 bacterial species (Escherichia coli, Klebsiella pneumoniae, Mannheimia haemolytica, Pasteurella multocida, Actinobacillus pleuropneumoniae, Staphylococcus aureus, Staphylococcus pseudintermedius, Staphylococcus hyicus, Streptococcus uberis, Streptococcus dysgalactiae and Streptococcus suis). Relevant antimicrobials for their treatment were selected (e.g. tetracyclines) and complemented with antimicrobials of more specific public health interest (e.g. carbapenems). Three test panels of antimicrobials were proposed covering most EARS-Vet combinations of relevance for veterinary antimicrobial stewardship. ConclusionsWith this scope, EARS-Vet would enable to better address animal health in the strategy to mitigate AMR and better understand the multi-sectoral AMR epidemiology in Europe.

microbiology↗

bioRxiv2018

Bayesian adjustment for trend of colorectal cancer incidence in misclassified registering across Iranian provinces

One of the problems in cancer registry of developing countries is misclassification error. This error leads to overestimation and underestimation of cancer rate in different provinces. The aim of this study is to use Bayesian method to correct for misclassification in registering cancer incidence in neighboring provinces of Iran. Incidence data of colorectal cancer were extracted from Iranian annual of national cancer registration reports 2005 to 2008 And Eighteen of the thirty Iranian provinces were selected to enter the Bayesian model and to correct their misclassification. Always a province with appropriate medical facilities is comparable to its neighbor or neighbors. Between years of 2005 and 2008, on the average, 28% misclassification was estimated between the province of East Azarbaijan and West Azarbayjan, 56% between the province of Fars and Hormozgan, 43% between the province of Isfahan and Charmahal and Bakhtyari, 46% between the province of Isfahan and Lorestan, 58% between the province of Razavi Khorasan and North Khorasan, 50% between the province of Razavi Khorasan and South Khorasan, 74% between the province of Razavi Khorasan and Sistan and Balochestan, 43% between the province of Mazandaran and Golestan, 37% between the province of Tehran and Qazvin, 45% between the province of Tehran and Markazi, 42% between the province of Tehran and Qom, 47% between the province of Tehran and Zanjan. Correcting the regional misclassification and obtaining the correct rates of cancer incidence in different regions is necessary for making cancer control and prevention programs and in healthcare resource allocation.

epidemiology↗