Outcomes of an intervention to improve hospital antibiotic prescribing: interrupted time series with segmented regression analysis

Objectives: To evaluate an intervention to reduce inappropriate use of key antibiotics with interrupted time series analysis.

Methods: The intervention is a policy for appropriate use of Alert Antibiotics (carbapenems, glycopeptides, amphotericin, ciprofloxacin, linezolid, piperacillin-tazobactam and third-generation cephalosporins) implemented through concurrent, patient-specific feedback by clinical pharmacists. Statistical significance and effect size were calculated by segmented regression analysis of interrupted time series of drug use and cost for 2 years before and after the intervention started.

Results: Use of Alert Antibiotics increased before the intervention started but decreased steadily for 2 years thereafter. The changes in slope of the time series were 0.27 defined daily doses/100 bed-days per month (95% CI 0.19-0.34) and pound1908 per month (95% CI pound1238-pound2578). The cost of development, dissemination and implementation of the intervention (pound20 133) was well below the most conservative estimate of the reduction in cost (pound133 296), which is the lower 95% CI of effect size assuming that cost would not have continued to increase without the intervention. However, if use had continued to increase, the difference between predicted and actual cost of Alert Antibiotics was pound572 448 (95% CI pound435 696-pound709 176) over the 24 months after the intervention started.

Conclusions: Segmented regression analysis of pharmacy stock data is a simple, practical and robust method for measuring the impact of interventions to change prescribing. The Alert Antibiotic Monitoring intervention was associated with significant decreases in total use and cost in the 2 years after the programme was implemented. In our hospital, the value of the data far exceeded the cost of processing and analysis.