IX.Add I.2.2. Increased ICSR reporting frequency

Most routine signal detection is aimed at identifying unknown, potentially causal associations between medicinal products and adverse events that are assumed to be constant over time. However, some causal associations of medicinal products with events of interest in the context of pharmacovigilance may show a marked temporal variation. Examples are manufacturing quality issues, a developing culture of abuse, evolving antimicrobial resistance or changes in the use of the product and, in particular, new off-label use. One way of detecting signals associated with such events, that may add value to simple disproportionality methods, is to monitor changes in the frequency of overall reporting for the products.

However, changes of reporting frequency are also expected that do not reflect new safety issues of the medicinal products. These may result from rapid increases in use when the product is first marketed or new indications are authorised, sudden changes in exposure (e.g. seasonal use of vaccines), publicity associated with unfounded safety concerns, reporting promoted by patient support schemes not clearly labelled as studies, clusters of ICSRs reported in the scientific literature or duplicated ICSR reports.

There are several options for detecting temporal changes in reporting frequency. The simplest method examines the changes in the number of ICSRs received per product over a fixed time period as an absolute count. Statistical tests compare recent counts with the latest count, testing for significant increases. Similar methods can be used at the DEC level and, for these, relative values compared to the total ICSR count for the product may be considered as an alternative to absolute counts. The method disregards however quantitative changes in exposure, which would impact on the frequency of adverse reactions.

Another option is to consider changes in the disproportionality statistics over time. This approach is less susceptible to increase in number of ICSRs triggered by effects related to the product rather than a specific adverse event – for example general publicity about the product, stimulated reporting (see GVP Annex I) or changes in exposure – however, results will still be influenced by the background distribution in the rest of the database and not only by changes in reporting frequency for the specific medicinal product. In addition, results might be less reactive to transient temporal variations since the focus is on changes in statistics based on the cumulative count, not in comparing recent counts with the latest count. This problem will be more pronounced when large numbers of cases have accumulated, as proportional changes will then be smaller.

Limited work has been performed to assess the effectiveness of these methods even if theoretically they seem appealing. Thus these methods might be implemented with ongoing quality control measures to ensure acceptable performance.