Limitations of estimating antibiotic resistance using German hospital consumption data - A comprehensive computational analysis

For almost a century, antibiotics have played an important role in the treatment of infectious diseases. However, the efficacy of these very drugs is now threatened by the development of resistances, which pose major challenges to medical professionals and decision-makers. Thereby, the consumption of antibiotics in hospitals is an important driver that can be targeted directly. To illuminate the relation between consumption and resistance depicts a very important step in this procedure. With the help of comprehensive ecological and clinical data, we applied a variety of different computational approaches ranging from classical linear regression to artificial neural networks to analyze antibiotic resistance in Germany. These mathematical and statistical models demonstrate that the amount and particularly the structure of currently available data sets lead to contradictory results and do, therefore, not allow for profound conclusions. More effort and attention on both data collection and distribution is necessary to overcome this problem. In particular, our results suggest that at least monthly or quarterly antibiotic use and resistance data at the department and ward level for each hospital (including application route and type of specimen) are needed to reliably determine the extent to which antibiotic consumption influences resistance development. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The Saxon State Ministry for Social Affairs and Social Cohesion (Saechsisches Staatsministerium fuer Soziales und Gesellschaftlichen Zusammenhalt) funded the Employment of Michael Rank as scientific collaborator at University Hospital Carl Gustav Carus Dresden at the TU Dresden, Division of Infectious Diseases from 03/2020-12/2022. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data sets and code used in this publication are available in the Zenodo repository under DOI 10.5281/zenodo.10684599.