ABSTRACT
Improving prescribing antibiotics appropriately for respiratory infections in primary care is an antimicrobial stewardship priority. There is limited evidence to support interventions to reduce prescribing antibiotics in out-of-hours (OOH) primary care. Herein, we report a service innovation where point-of-care C-Reactive Protein (CRP) machines were introduced to three out-of-hours primary care clinical bases in England from August 2018-December 2019, which were compared with four control bases that did not have point-of-care CRP testing. We undertook a mixed-method evaluation, including a comparative interrupted time series analysis to compare monthly antibiotic prescription rates between bases with CRP machines and those without, an analysis of the number of and reasons for the tests performed, and qualitative interviews with clinicians. Antibiotic prescription rates declined during follow-up, but with no clear difference between the two groups of out-of-hours practices. A single base contributed 217 of the 248 CRP tests performed. Clinicians reported that the tests supported decision making and communication about not prescribing antibiotics, where having 'objective' numbers were helpful in navigating non-prescribing decisions and highlighted the challenges of training a fluctuant staff group and practical concerns about using the CRP machine. Service improvements to reduce prescribing antibiotics in out-of-hours primary care need to be developed with an understanding of the needs and context of this service.
ABSTRACT
BACKGROUND: In diagnostic evaluation, it is necessary to assess the clinical impact of a new diagnostic as well as its diagnostic accuracy. The comparative interrupted time series design has been proposed as a quasi-experimental approach to evaluating interventions. We show how it can be used in the design of a study to evaluate a point-of-care diagnostic test for C-reactive protein in out-of-hours primary care services, to guide antibiotic prescribing among patients presenting with possible respiratory tract infection. This study consisted of a retrospective phase that used routinely collected monthly antibiotic prescribing data from different study sites, and a prospective phase in which antibiotic prescribing rates were monitored after the C-reactive protein diagnostic was introduced at some of the sites. METHODS: Of 8 study sites, 3 were assigned to receive the diagnostic and 5 were assigned as controls. We obtained retrospective monthly time series of respiratory tract targeted antibiotic prescriptions at each site. Separate ARIMA models at each site were used these to forecast monthly prescription counts that would be expected in the prospective phase, using simulation to obtain a set of 1-year predictions alongside their standard errors. We show how these forecasts can be combined to test for a change in prescription rates after introduction of the diagnostic and estimate power to detect this change. RESULTS: Fitted time series models at each site were stationary and showed second-order annual seasonality, with a clear December peak in prescriptions, although the timing and extent of the peak varied between sites and between years. Mean one-year predictions of antibiotic prescribing rates based on the retrospective time series analysis differed between sites assigned to receive the diagnostic and those assigned to control. Adjusting for the trend in the retrospective time series at each site removed these differences. CONCLUSIONS: Quasi-experimental designs such as comparative interrupted time series can be used in diagnostic evaluation to estimate effect sizes before conducting a full randomised controlled trial or if a randomised trial is infeasible. In multi-site studies, existing retrospective data should be used to adjust for underlying differences between sites to make outcome data from different sites comparable, when possible.
