New approaches to strengthen pharmacovigilance

Key pharmacovigilance stakeholders' experiences of direct patient reporting of adverse drug reactions and their prospects of future development in the European Union

OBJECTIVES

In the European Union (EU), legislation allows patients to directly report adverse drug reactions (ADRs) to competent authorities. Five years after its implementation, patient reporting is not equal in all countries. This study aimed to explore key stakeholders' perceptions of patient reporting in four EU countries.

STUDY DESIGN

Qualitative study design.

METHODS

Twelve representatives from national pharmacovigilance centres and/or authorities as well as national pharmaceutical industry bodies in four EU countries participated in the study. Supranational organizations were also included. Data collection was via face-semi-structured interviews. Inductive content analysis was performed thereafter, applying principles of risk management as a theoretical framework.

RESULTS

Four themes (attitudes and beliefs, system maturation factors, regulatory improvements, and cultural shifts) emerged, conceptually interconnected. Participants from countries introducing patient reporting recently expressed a negative attitude. Participants highlighted the need for additional resources, both human and financial, to address patient reporting and associated advantages.

CONCLUSIONS

The findings identified perceived barriers and facilitators of patient reporting. The involvement of patients, use of information, and dissemination of patient reporting are far from optimal. A better integration of the work by EU regulatory authorities is recommended.

A comparison of active adverse event surveillance systems worldwide

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.

Signal detection and monitoring based on longitudinal healthcare data

Post-marketing detection and surveillance of potential safety hazards are crucial tasks in pharmacovigilance. To uncover such safety risks, a wide set of techniques has been developed for spontaneous reporting data and, more recently, for longitudinal data. This paper gives a broad overview of the signal detection process and introduces some types of data sources typically used. The most commonly applied signal detection algorithms are presented, covering simple frequentistic methods like the proportional reporting rate or the reporting odds ratio, more advanced Bayesian techniques for spontaneous and longitudinal data, e.g., the Bayesian Confidence Propagation Neural Network or the Multi-item Gamma-Poisson Shrinker and methods developed for longitudinal data only, like the IC temporal pattern detection. Additionally, the problem of adjustment for underlying confounding is discussed and the most common strategies to automatically identify false-positive signals are addressed. A drug monitoring technique based on Wald's sequential probability ratio test is presented. For each method, a real-life application is given, and a wide set of literature for further reading is referenced.