| Summary: | Tackling the problem of rising antibiotic resistance requires valid and comparable data on the use of antimicrobial drugs in livestock. To date, no harmonized monitoring of antimicrobial usage in animals is available, and there is no system to assess usage data throughout Europe, thus hampering a direct comparison between different European countries. Most of the currently applied monitoring systems are based on sales data. Placement of sales data in relation to the population at risk requires overall assumptions about the weights of the animals treated and the doses applied. Only a few monitoring systems collect data in which the number of treated animals is reported exactly and does not need to be estimated. To evaluate the influence of different calculation methods on the standardizing procedure of antibiotic usage and benchmarking of farms, the treatment frequency for several farms (broiler, suckling piglets, and fattening pigs) was calculated in the following two different ways: first, based on the Used Daily Dose (TFUDD), and second, based on the Defined Daily Dose (TFDDD). To support this evaluation, consumption data from the Veterinary Consumption of Antibiotics Sentinel (VetCAb-S) project in Germany were used as example data. The results show discrepancies between both outcomes depending on the calculation method applied. In broiler holdings, the median values of TFDDD were 20.89% lower than the median values of TFUDD. In suckling piglets and fattening pig holdings, the median values of TFDDD were increased 77.14% and 16.33%, respectively, which may have serious implications for the benchmarking of farms. Furthermore, this finding reflects that the calculation procedure also has an impact on the comparison between populations. Therefore, UDD-based calculations should be preferred to run monitoring systems with a benchmark mission. If, in contrast, the DDD approach is chosen to compare antimicrobial usage between populations, additional considerations should be made to adjust for the addressed discrepancies.
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