Benefits and strengths of the disproportionality analysis for identification of adverse drug reactions in a pharmacovigilance database

Abstract

Adverse drug reactions (ADRs) represent an important medical issue: they result in 3–7% of all hospital admissions and are associated with a substantial increase in morbidity and mortality. Numerous methods can be used to investigate ADRs. Each has its strengths and weakness. The insufficiencies of basic (experimental) pharmacology as well as clinical trials for studying ADRs are well known. Animal physiology often differs from that of humans. Clinical trials, although necessary, do not allow definite conclusions, because they are built to evaluate efficacy more than safety. Thus, spontaneous notifications remain the cornerstone for ADRs despite their mandatory limitations (under-reporting, selective reporting, lack of denominator etc.). Intensive studies could allow quantification of a specific problem of drug safety (i.e. drug admission into hospital for ADRs). For some years, several pharmacoepidemiological methods have been used to identify and also to quantify ADRs. Among thesse methods, case–control or cohort studies are most widely used. However, their setting up requires specific organization, delay, money and also use of large databases, which are not often specifically built for evaluation of ADRs. In the present issue of the journal, clinical pharmacologists and pharmacoepidemiologists from Poitiers University Hospital (France) have used another method, working from the French PharmacoVigilance database. They used disproportionality analysis for identification of memory disorders associated with drugs [1]. The present paper discusses the benefits and strengths of this method. As far as we know, the first time this approach was used was in the early 1980s in the field of drug safety during pregnancy. The question of a possible relation between valproic acid and spina bifida aperta was investigated by Robert [2] after a cluster of case reports in France from the Birth Defects Monitoring system in the Rhone-Alpes region (East France). Robert compared exposure to valproic acid in 146 women with infants suffering from spina bifida aperta and in 6616 other mothers with infants suffering from different malformations. They found a strong significant association, with an odds ratio of 20 and a P value <0.00001. This approach, called ‘case–control study’ in the original paper, was really the first performed case–noncase study. Following this first signal, further studies confirmed the teratogenicity of valproic acid in the first trimester of pregnancy. The second historical example was the investigation of a potential higher risk of serum-sickness-like syndrome related to cefaclor [3]. After the report of several cases in the early 1990s, Stricker and Tijssen performed a ‘nested case–control study’ in the WHO Collaborating Centre for International Drug Monitoring Database, including reports from the USA, UK, Sweden, Canada and Germany for the period 1968–1987. They defined an ADR reporting odds ratio (ROR) as the ratio of the odds of the number of ADR reports of serum sickness in relation to cefaclor and amoxicillin or cephalexin and the odds of other reports on the same drugs. Using this case–noncase approach, they were able to identify that cefaclor had a significantly higher risk for serum-sickness-like syndrome compared with the two other antibiotics. During the 1990s, the databases collecting suspected ADR reports had grown, reaching sizes of more than several thousands or millions, thus making routine and regular quantitative screening a necessity. This data mining in these large databases has become a necessity in order to help pharmacovigilance systems to identify early signals for specific ADRs. Various statistical measures have been proposed for the application of computer-assisted quantitative signal detection procedures. Data mining encompasses a number of statistical techniques, including cluster analysis, link analysis, deviation detection and disproportionality assessment, which can be used to determine the presence of and to assess the strength of ADR signals. The use of a measure of disproportionality is currently applied in various national spontaneous reporting centres, as well as in the WHO Monitoring Centre. Several point estimates, such as ROR and proportional ADR reporting ratio (PRR), have been proposed, in order to analyse, for example, the UK Yellow Card Scheme spontaneous reporting database. Furthermore, the chance of the number of reports being reported in a certain combination with the assumption that no relation exists between the reported suspected ADR and the suspected medication can be calculated by means of the Poisson probability. Another approach is the use of Bayesian logic, specifying the relation between the prior and posterior probability before and after linking data fields, and of adding new data to the database. This method is currently used, for example by the WHO Monitoring Centre in the Bayesian confidence propagation neural network analysis (BCPNN). The statis