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Record W4411363608 · doi:10.1007/s00228-025-03862-2

Trends in adverse drug reaction reporting in eight selected countries after the implementation of new pharmacovigilance regulation in 2012: a joinpoint regression analysis

2025· article· en· W4411363608 on OpenAlexaboutno aff
Edgaras Stankevıčıus, Edmundas Kaduševičius

Bibliographic record

VenueEuropean Journal of Clinical Pharmacology · 2025
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsnot available
FundersCollege ter Beoordeling van Geneesmiddelen
KeywordsPharmacovigilanceDrugDrug reactionPharmacologyAdverse drug reactionMedicineAdverse effect

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.109
GPT teacher head0.528
Teacher spread0.419 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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