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



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).


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.


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.


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.


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.

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The authors thank Brigitta Osterberger at the TGA for her help in editing the manuscript.

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Corresponding author

Correspondence to Clare E. King.

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Author Contributions

All authors contributed to study conception and design. Data collection and analysis were performed by LT and NC, with the statistical code provided by NLP. Interpretation of the results was performed by CEK, MW, NN, ECB, NLP and LKE. The first draft of the manuscript was written by CEK and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability Statement

The list of PSSA signals for furosemide initiation is provided in Appendix A in the Electronic Supplementary Materials. No additional data available. The data that support the findings of this study are available from the Australian Government Department of Health but restrictions apply to the availability of these data, and so are not publicly available.


NP was funded by a National Health and Medical Research Centre (NHMRC) Grant, Centre of Research Excellence in post-marketing surveillance of medicines and medical devices GNT 1040938 and NHMRC Project Grant GNT 1157506. LKE was supported by an NHMRC-ARC Dementia Research Development Fellowship (Grant identification number APP1101788).

Conflict of interest

All authors have no conflicts of interest that are directly relevant to the content of the manuscript.

Ethics approval

The PBS data fields used in the study were medicine item code, supply date of medicine and patient identification (ID) key. As the patient ID key was a unique data key rather than a number that can be used to re-identify individuals, the research was considered negligible risk under the National Statement on Ethical Conduct in Human Research. The project was discussed with the Department of Health Human Research Ethics Committee, but formal ethics approval was not required.

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The results of the study have not been published previously.

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King, C.E., Pratt, N.L., Craig, N. et al. Detecting Medicine Safety Signals Using Prescription Sequence Symmetry Analysis of a National Prescribing Data Set. Drug Saf (2020).

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