Principles of signal detection in pharmacovigilance

Adverse drug effects are manifold and heterogenous. Many situations may hamper the signalling (i.e. the detection of early warning signs) of adverse effects and new signals often differ from previous experiences. Signals have qualitative and quantitative aspects. Different categories of adverse effects need different methods for detection. Current pharmacovigilance is predominantly based on spontaneous reporting and is mainly helpful in detecting type B effects (those effects that are often allergic or idiosyncratic reactions, characteristically occurring in only a minority of patients and usually unrelated to dosage and that are serious, unexpected and unpredictable) and unusual type A effects (those effects that are related to the pharmacological effects of the drug and are dosage-related). Examples of other sources of signals are prescription event monitoring, large automated data resources on. morbidity and drug use (including record linkage), case-control surveillance and follow-up studies. Type C effects (those effects related to an increased frequency of 'spontaneous' disease) are difficult to study, however, and continue to pose a pharmacoepidemiological challenge. Seven basic considerations can be identified that determine the evidence contained in a signal: quantitative strength of the association, consistency of the data, exposure response relationship, biological plausibility, experimental findings, possible analogies and the nature and quality of the data. A proposal is made for a standard signal management procedure at pharmacovigilance centres, including the following steps: signal delineation, literature search, preliminary inventory of data, collection of additional information, consultation with the World Health Organization Centre for International Drug Monitoring and the relevant drug companies, aggregated data assessment and a report in writing. A better understanding of the conditions and mechanisms involved in the detection of adverse drug effects may further improve strategies for pharmacovigilance.