Communicating post-market safety risks of medicines with regulatory safety advisories: an international comparison of policy and perceptions
Bibliographic record
Abstract
Background Information about the safety of medicines often emerges after approval. Medicines’ regulators use post-market safety advisories to communicate potential new harms. Advisories can influence medicines use, helping users to weigh benefits and harms. This thesis compared regulatory policy and outcomes for post-market safety communication in Australia, Canada, the United Kingdom (as part of the European Union) and the United States (US). Methods The four regulators were compared using: • A regulatory policy analysis. • An in-depth case study of safety communications for SGLT2 inhibitors (2012-2018). • A content analysis of safety advisories issued for new drugs approved in Australia 2010-2016. • Qualitative interviews exploring prescriber awareness and use of medicines safety information (Boston and Australia). Results Differences in regulatory policy among the European Medicines Agency, the US Food and Drug Administration, Health Canada, and the Therapeutic Goods Administration (TGA) included: their legislated authority for safety advisories, transparency, and interactions with pharmaceutical industry. SGLT2 inhibitor safety advice differed among regulators in number, timing, and strength. TGA advisories were issued for 20.5% of 73 safety concerns communicated by other regulators, for new drugs approved in Australia (2010-2016). Differences were not explained by the seriousness of safety concerns. Prescribers’ awareness of regulatory safety advisories was relatively low, particularly in Australia. While respecting regulators’ institutional authority, regulatory warnings may lack clinical authority. Conclusions There are considerable differences amongst the EMA, FDA, Health Canada and the TGA in policy and use of post-market safety advisories. Recommendations for improving safety and policy are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".