Time Series Methods to Assess the Impact of Regulatory Action: A Study of UK Primary Care and Hospital Data on the Use of Fluoroquinolones.

Abstract

Purpose: To illustrate the interest in using interrupted time series (ITS) methods, this study evaluated the impact of the UK MHRA's March 2019 Risk Minimisation Measures (RMM) on fluoroquinolone usage.

Methods: Monthly and quarterly fluoroquinolone use incidence rates from 2012 to 2022 were analysed across hospital care (Barts Health NHS Trust), primary care (Clinical Practice Research Datalink (CPRD) Aurum and CPRD GOLD), and linked records from both settings (East Scotland). Rates were stratified by age (19-59 and ≥ 60 years old). Seasonality-adjusted segmented regression and ARIMA models were employed to model quarterly and monthly rates, respectively.

Results: Post-RMM, with segmented regression, both age groups in Barts Health experienced nearly complete reductions (> 99%); CPRD Aurum saw 20.19% (19-59) and 19.29% ( $\ge$  60) reductions; no significant changes in CPRD GOLD; East Scotland had 45.43% (19-59) and 41.47% ( $\ge$  60) decreases. Slope analysis indicated increases for East Scotland (19-59) and both CPRD Aurum groups, but a decrease for CPRD GOLD's $\ge$  60; ARIMA detected significant step changes in CPRD GOLD not identified by segmented regression and noted a significant slope increase in Barts Health's 19-59 group. Both models showed no post-modelling autocorrelations across databases, yet Barts Health's residuals were non-normally distributed with non-constant variance.

Conclusions: Both segmented regression and ARIMA confirmed the reduction of fluoroquinolones use after RMM across four different UK primary care and hospital databases. Model diagnostics showed good performance in eliminating residual autocorrelation for both methods. However, diagnostics for hospital databases with low incident use revealed the presence of heteroscedasticity and non-normal white noise using both methods.