A Comparison of Active Adverse Event Surveillance Systems Worldwide
Bibliographic record
Abstract
Post-marketing drug surveillance for adverse drug events (ADEs) has typically relied on spontaneous reporting. Recently, regulatory agencies have turned their attention to more preemptive approaches that use existing data for surveillance. We conducted an environmental scan to identify active surveillance systems worldwide that use existing data for the detection of ADEs. We extracted data about the systems' structures, data, and functions. We synthesized the information across systems to identify common features of these systems. We identified nine active surveillance systems. Two systems are US based-the FDA Sentinel Initiative (including both the Mini-Sentinel Initiative and the Federal Partner Collaboration) and the Vaccine Safety Datalink (VSD); two are Canadian-the Canadian Network for Observational Drug Effect Studies (CNODES) and the Vaccine and Immunization Surveillance in Ontario (VISION); and two are European-the Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge (EU-ADR) Alliance and the Vaccine Adverse Event Surveillance and Communication (VAESCO). Additionally, there is the Asian Pharmacoepidemiology Network (AsPEN) and the Shanghai Drug Monitoring and Evaluative System (SDMES). We identified two systems in the UK-the Vigilance and Risk Management of Medicines (VRMM) Division and the Drug Safety Research Unit (DSRU), an independent academic unit. These surveillance systems mostly use administrative claims or electronic medical records; most conduct pharmacovigilance on behalf of a regulatory agency. Either a common data model or a centralized model is used to access existing data. The systems have been built using national data alone or via partnership with other countries. However, active surveillance systems using existing data remain rare. North America and Europe have the most population coverage; with Asian countries making good advances.
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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 | 0.027 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".