Impact analysis of signals detected from spontaneous adverse drug reaction reporting data

Abstract

This paper describes a new method of prioritising signals of potential adverse drug reactions (ADRs) detected from spontaneous reports that is called impact analysis. This is an interim step between signal detection and detailed signal evaluation. Using mathematical screening tools, large numbers of signals may now be detected from spontaneous ADR databases. Regulatory authorities need to rapidly prioritise them and focus on those that are most likely to require significant action. Using two scores ranging from one to 100, each with three input variables, signals may be categorised in terms of the strength of evidence (E) and the potential public health impact (P). In a two-by-two figure with empirically derived cut-off points of ten (the logarithmic mean) for each score, signals are placed in one of four categories (A-D) that are ranked according to their priority (A being the highest and D the lowest). A sensitivity analysis is then performed that tests the robustness of the categorisation in relation to each of the six input variables. A computer program has been written to facilitate the process and reduce error. Further work is required to test the feasibility and value of impact analysis in practice.