Abstract / Summary
Objectives: Variation in antibiotic prescribing for common infections between general practices in the UK may be partly explained by differences in case-mix. We aimed to quantify how much of this variation was attributable to differences in prescribing norms between practices after accounting for case-mix. Methods: We used the UK Clinical Practice Research Datalink (CPRD) Aurum database to identify general practitioner consultations for 11 common infections. Three-way variance decomposition quantified the proportions of total variance attributable to patient case-mix, between practice variation, and residual unexplained variance across three models (no, minimal (age/sex), and full case mix adjustment). For lower respiratory tract infection and sore throat, external data to impute illness severity were used to estimate the potential effect of unmeasured infection severity. Results: We identified 3820,806 consultations in 2019. There was clear variability in antibiotic prescribing across practices for most conditions. In fully adjusted models, between-practice variation explained 5.8–32.6% of total variance, exceeding variation attributed to case-mix in 9 out of 11 infections. Acute otitis media and upper respiratory tract infection (URTI), conditions with substantial prescribing variations across practices and high variance attributable to adjusted between-practice differences (12.6% and 10.3%, respectively), are promising candidates for fair prescribing indicators. Compared to no adjustment, full case-mix adjustment lowered the contribution of between-practice differences to total variance, while minimal age–sex adjustment had little impact. Imputing infection severity in addition to full case-mix adjustment further reduced contribution of between-practice variance to the total variance. Conclusions: Differences in practice-level prescribing, beyond patient case-mix, highlight the value of providing feedback and antibiotic stewardship at the practice level. Comprehensive case-mix adjustment, including imputed infection severity, supports fairer performance comparisons.