Objectives: To identify the rates of potentially inappropriate antibiotic choice when prescribing for common infections in UK general practices. To examine the predictors of such prescribing and the clustering effects at the practice level.
Methods: The rates of potentially inappropriate antibiotic choice were estimated using 1 151 105 consultations for sinusitis, otitis media and externa, upper respiratory tract infection (URTI) and lower respiratory tract infection (LRTI) and urinary tract infection (UTI), using the Clinical Practice Research Datalink (CPRD). Multilevel logistic regression was used to identify the predictors of inappropriate prescribing and to quantify the clustering effect at practice level.
Results: The rates of potentially inappropriate prescriptions were highest for otitis externa (67.3%) and URTI (38.7%) and relatively low for otitis media (3.4%), sinusitis (2.2%), LRTI (1.5%) and UTI in adults (2.3%) and children (0.7%). Amoxicillin was the most commonly prescribed antibiotic for all respiratory tract infections, except URTI. Amoxicillin accounted for 62.3% of prescriptions for otitis externa and 34.5% of prescriptions for URTI, despite not being recommended for these conditions. A small proportion of the variation in the probability of an inappropriate choice was attributed to the clustering effect at practice level (8% for otitis externa and 23% for sinusitis). Patients with comorbidities were more likely to receive a potentially inappropriate antibiotic for URTI, LRTI and UTI in adults. Patients who received any antibiotic in the 12 months before consultation were more likely to receive a potentially inappropriate antibiotic for all conditions except otitis externa.
Conclusions: Antibiotic prescribing did not always align with prescribing guidelines, especially for URTIs and otitis externa. Future interventions might target optimizing amoxicillin use in primary care.
© The Author(s) 2019. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.