Trends in adverse drug reaction reporting in eight selected countries after the implementation of new pharmacovigilance regulation in 2012: a joinpoint regression analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Underreporting of adverse drug reactions (ADRs) remains to be a challenge in modern health care. Major reforms in the EU pharmacovigilance system in 2012 introduced the legal basis for consumers to report suspected ADRs. This study was designed to determine trends in overall ADR reporting, the ratio of health care professional (HCP)-to-consumer ADR reporting, and the ratio of serious-to-nonserious ADR reporting for an 11-year period in the selected non-EU and EU countries that had implemented systems for consumers' reporting before the 2012 EU pharmacovigilance legislation and those that did not have. MATERIALS AND METHODS: The national competent authorities of 15 countries (11 EU countries, former EU member the UK, Australia, Canada, and the USA) were contacted via e-mail and asked to provide the total number of ADR reports, numbers of ADRs reported by consumers and HCPs, numbers of serious and nonserious ADRs, and top 5 medication groups causing ADRs by the Anatomical Therapeutic Chemical (ATC) classification system during the period of 2012-2022. Eight countries, namely Belgium, Canada, Finland, Lithuania, the Netherlands, Portugal, Sweden, and the UK, responded and provided the data. The trends of ADR reporting were evaluated with the joinpoint regression analysis method. The annual percent change (APC) and the average annual percent change (AAPC) were estimated. RESULTS: Over the study period, the overall rates of ADR reporting increased significantly in all the countries except for Belgium, with the greatest AAPC being in Lithuania (AAPC of 32.34) and the lowest, in Canada (AAPC of 10.3). The ratios of HCP-to-consumer ADR reporting were significantly decreasing in all the countries (AAPC range, -43.7 to -24.9) except for Canada where an opposite significant trend toward an increasing HCP reporting rate (AAPC of 3.9) for 2012-2020 was observed. The ratios of serious-to-nonserious ADR reporting were significantly decreasing in more than half of the countries, namely Canada, Finland, Lithuania, the Netherlands, and Portugal, with the greatest negative AAPC being in Lithuania (AAPC of -32.9) and the smallest, in Canada (AAPC of -6.8). Vaccines (J07), immunosuppressants (L04), antineoplastic agents (L01), antibacterials for systemic use (J01), and antithrombotic agents (B01) were found to be the top 5 most frequently reported medications. CONCLUSIONS: This study shows the significant upward trends in overall ADR reporting not only in the countries that implemented consumer ADR reporting systems after the 2012 EU pharmacovigilance legislation but also in countries that had consumer reporting systems before 2012. Moreover, significant downward trends in the ratios of HCP-to-consumer ADR reporting were documented for all EU countries, confirming increasing consumers' involvement in ADR reporting. Further, larger scale studies with the involvement of more countries are needed to better understand the trends in ADR reporting, and multifaceted interventions are warranted to be installed to enhance ADR reporting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".