25 Trends in antibiotic selection pressure generated in primary care and their association with sentinel antibiotic resistance patterns in Europe 2 subgroup. The cumulative ASI (i.e. cumulative antibiotic spectrum index per 1000 inhabitants) in a country was calculated by adding up the ASIs of each subgroup. For each country, this was calculated for (i) primary care, (ii) hospital care and (iii) primary and hospital care combined (i.e. the combined cumulative ASI). AMR of sentinel micro organisms AMR surveillance systems can use a set of drug-resistant micro organisms rather than a complete overview of micro organisms to monitor trends in AMR (16). This approach was taken and three so-called SDRMs relevant for primary care were selected: Staphylococcus aureus, Escherichia coli and Streptococcus pneumoniae are often used to monitor AMR (16). MRSA was used because S. aureus is the leading cause of skin and soft tissue infections. From the order Enterobacterales, E. coli resistant to third-generation cephalosporins and fluoroquinolones and aminoglycosides was selected, because E. coli is the leading pathogen causing urinary tract infections. S. pneumoniae is the most common cause of community-acquired bacterial pneumonia and was considered resistant if non-susceptible to macrolides. We chose to select nonsusceptibility to macrolides instead of resistance to penicillin. Macrolides are regularly second-choice antibiotics for the treatment of community-acquired pneumonia in primary care guidelines, making it a reserved antibiotic only used where other antibiotics are not effective or administrable (17). Country-level prevalences of the three SDRMs were obtained from the ECDC open source database, Surveillance Atlas Antimicrobial resistance, on 2 March 2022 for the years 2011–2020 (11). The ECDC uses the EUCAST guidelines for detecting and reporting specific resistant micro organisms. Treatment of infections in primary care is most often empirical, and obtaining cultures is therefore not part of standard care and not always feasible due to practical reasons. Anticipating a lack of SDRM cultures available from primary care, we combined primary and hospital care data to characterize AMR in each country because, according to the One Health concept, all antibiotic prescriptions contribute to ASP and eventually to AMR (8). Descriptive statistics were used to describe and compare antibiotic volumes between countries and periods, as well as the trends in the volume of antibiotic prescriptions, and the prevalences of SDRMs. The combined cumulative ASI and combined DDD were plotted against the prevalence of each SDRM per country for the year 2020, because it is the most recent year with available data. Univariate linear regression was used to calculate associations between (i) ASI and (ii) DDD and each SDRM prevalence.
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