BACKGROUND AND AIMS:Over-the-counter codeine products were up-scheduled to prescription only in Australia from February 2018. This trend study aimed to identify changes in codeine supply before and after the February 2018 implementation.DESIGN, SETTING AND CASES:Time-series regression analysis of monthly medicine supplies in Australia from 2014 to 2018. The February 2018 up-scheduling was pre-specified as the intervention; outlier analysis was used to detect automatically sudden unexpected changes before February 2018.MEASUREMENTS:Per-capita supplies based on national data for pharmaceutical wholesales and population exposure. Weight of supplies in milligrams for low-dose codeine (≤ 15 mg per tablet or ≤ 1.92 mg per ml, originally sold over the counter but up-scheduled after February 2018), high-dose combination codeine (30 mg per tablet, prescription only throughout the study period) and all codeine.FINDINGS:Several level shifts in supply occurred during the 5 years, led by one of -4.4% [95% confidence interval (CI) = -6.6 to -2.1%] in high-dose codeine in 2015, followed by shifts in low-dose codeine of -40.0% (CI = -46.9 to -32.3%) and -82.2% (CI = -84.3 to -79.9%), respectively, before and after February 2018. High-dose codeine supply increased by 4.4% (CI = 1.8-7.1%) immediately after up-scheduling. Also detected were transient increases and decreases in 2016 and 2017. Compared with pre-2015 levels, the February 2018 up-scheduling was associated with reductions of 45.7% (CI = 43.2-48.0%) and 89.3% (CI = 87.9-90.6%), respectively, in all and low-dose codeine supply but no change in high-dose codeine supply. The level shifts and transient changes were located around various regulatory activities, including public announcements and expert advisory meetings on up-scheduling.CONCLUSION:Up-scheduling of over-the-counter codeine products in Australia in 2018 appears to have been associated with a near halving of Australia's national codeine supply. The transition occurred in multiple forms and phases.

Introduction Medicine safety signal detection methods employed by the medicine regulator in Australia (Therapeutic Goods Administration [TGA], Department of Health) rely predominantly on analysis of spontaneous adverse event (AE) reports, sponsor notifications or information shared by international agencies. The limitations of these methods and the availability of large administrative health data sets has given rise to greater interest in the use of administrative health data to support pharmacovigilance (PV). Objective We explored whether prescription sequence symmetry analysis (PSSA) of Pharmaceutical Benefits Scheme (PBS) data can enhance signal detection by the TGA, using the AE, heart failure (HF) as a case study. Methods We applied the PSSA method to all single-ingredient medicines dispensed under the PBS between 2012 and 2016, using furosemide initiation as a proxy for new-onset HF. A signal was considered present if the lower limit of the 95% confidence interval for the adjusted sequence ratio was > 1. We excluded medicines known to cause HF, indicated for HF treatment or indicated for diseases that may contribute to HF. Results Of the 654 tested medicines, 26 potential new HF signals were detected by PSSA. Five signals had additional support for the possible association provided by biological plausibility, consistency and disproportionate reporting of cases of HF to the TGA and the World Health Organization; and clinical impact. Conclusion PSSA was able to identify potential signals for further evaluation. With the increasing availability of different administrative health data sources, the strengths and weaknesses of methods used to analyse these data for the purpose of regulatory PV should be evaluated.

Method: Data for individual outbreaks was collected from the Metro South Public Health Unit (MSPHU) internal database and evaluated using descriptive epidemiology. Outcomes measured included duration of outbreak, delay to notification, total case numbers, staff and resident case breakdown, pathogens identified, hospitalisations and deaths. Average and median values for the four-year time periods were calculated from annual means and medians of each year.