Detecting Medicine Safety Signals Using Prescription Sequence Symmetry Analysis of a National Prescribing Data Set

Clare E. King (), Nicole L. Pratt, Nichole Craig, Loc Thai, Margaret Wilson, Neillan Nandapalan, Lisa Kalisch Ellet and Eirene C. Behm
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Clare E. King: Therapeutic Goods Administration, Australian Government Department of Health
Nicole L. Pratt: University of South Australia
Nichole Craig: Australian Government Department of Health
Loc Thai: Australian Government Department of Health
Margaret Wilson: Therapeutic Goods Administration, Australian Government Department of Health
Neillan Nandapalan: Therapeutic Goods Administration, Australian Government Department of Health
Lisa Kalisch Ellet: University of South Australia
Eirene C. Behm: Therapeutic Goods Administration, Australian Government Department of Health

Drug Safety, 2020, vol. 43, issue 8, No 10, 787-795

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1007/s40264-020-00940-5

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