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Introduction: The case-population approach compares exposure among cases to that of their source population. By using aggregated data to estimate the denominator, this approach can provide a real-time estimate of an association that could be particularly valuable to explore urgent vaccine safety concerns and to generate signals during a vaccine campaign., Objective: Our objective was to present the vaccine case-population method, a method derived from the case-population approach and adapted for vaccine safety surveillance, and to test it using several published examples., Methods: For the vaccine case-population method, exposure in the population is estimated from the sum of at-risk periods using the number of vaccinated individuals, or data of vaccine sales, and the at-risk period considered for the vaccine-event pair. The vaccine case-population method was applied to data from published case-control studies retrieved from the MEDLINE database and having quantified risks associated with vaccines. Odds ratios derived from the vaccine case-population method were compared with those from published case-control studies., Results: A total of 20 vaccine-event pairs were retrieved in which the vaccine case-population method could be applied. For all identified vaccine-event pairs, when a significant association was found using the vaccine case-population method, a significant association was also found in the corresponding case-control study. Conversely, when no association was found by the vaccine case-population method, no association was found in the corresponding case-control study., Conclusion: These results suggest that the vaccine case-population method can produce coherent conclusions and may be used in the future for prospective investigation of urgent vaccine safety concerns or for the prospective generation of vaccine safety signals. This method could also be used to identify selection bias from cases excluded from the case-control study.

Introduction: The two methods for minimizing competition bias in signal of disproportionate reporting (SDR) detection--masking factor (MF) and masking ratio (MR)--have focused on the strength of disproportionality for identifying competitors and have been tested using competitors at the drug level., Objectives: The aim of this study was to develop a method that relies on identifying competitors by considering the proportion of reports of adverse events (AEs) that mention the drug class at an adequate level of drug grouping to increase sensitivity (Se) for SDR unmasking, and its comparison with MF and MR., Methods: Reports in the French spontaneous reporting database between 2000 and 2005 were selected. Five AEs were considered: myocardial infarction, pancreatitis, aplastic anemia, convulsions, and gastrointestinal bleeding; related reports were retrieved using standardized Medical Dictionary for Regulatory Activities (MedDRA(®)) queries. Potential competitors of AEs were identified using the developed method, i.e. Competition Index (ComIn), as well as MF and MR. All three methods were tested according to Anatomical Therapeutic Chemical (ATC) classification levels 2-5. For each AE, SDR detection was performed, first in the complete database, and second after removing reports mentioning competitors; SDRs only detected after the removal were unmasked. All unmasked SDRs were validated using the Summary of Product Characteristics, and constituted the reference dataset used for computing the performance for SDR unmasking (area under the curve [AUC], Se)., Results: Performance of the ComIn was highest when considering competitors at ATC level 3 (AUC: 62 %; Se: 52 %); similar results were obtained with MF and MR., Conclusion: The ComIn could greatly minimize the competition bias in SDR detection. Further study using a larger dataset is needed.

Introduction: Spontaneous reporting systems (SRSs) are pivotal for signal detection, especially for rare events with a high drug-attributable component, such as torsade de pointes (TdP). Use of different national SRSs is rarely attempted because of inherent difficulties, but should be considered on the assumption that rare events are diluted in international databases., Objective: The aim was to describe TdP-related events associated with antipsychotics, H1-antihistamines and anti-infectives in three national SRSs (in Italy, Germany and France) and highlight potential signals of torsadogenicity through a combined literature evaluation., Methods: A common search strategy was applied to extract TdP-related events: (1) TdP, (2) QT interval abnormalities, (3) ventricular fibrillation/tachycardia, and (4) sudden cardiac death. Signals of disproportionate reporting (SDRs) were calculated for TdP + QT interval abnormalities and defined by a lower limit of the 95 % confidence interval of the reporting odds ratio (ROR) >1. Among SDRs with at least three cases without concomitant pro-arrhythmic drugs, we defined potential new signal of torsadogenicity as drugs with no published evidence from (a) the crediblemeds(®) website ( http://www.crediblemeds.com , as of November 1st, 2014); (b) studies on the FDA Adverse Event Reporting System (FAERS); and (c) safety trials or pharmaco-epidemiological studies (as of December 16th, 2014)., Results: Overall, 3505 cases were retrieved (1372, 1468, and 801 for France, Germany and Italy, respectively). Antipsychotics were mainly recorded in Germany (792 cases), whereas antibiotics peaked at 515 and 491 (France and Italy, respectively). Forty-one drugs met criteria for SDRs in at least one single source, of which 31 were detected only from one single SRS: 18, ten and three (French, German and Italian SRS, respectively). By contrast, only five SDRs were detected in all national data sources (amisulpride, aripiprazole, haloperidol, olanzapine, risperidone). Overall, five potential new signals of torsadogenicity were identified: flupentixol, ganciclovir, levocetirizine, oxatomide and tiapride., Conclusions: We found differences across and within national SRSs in the reporting of drug-induced TdP, which finally resulted in five potential new signals of torsadogenicity. These findings warrant targeted pharmacovigilance studies to formally assess the existence of actual drug-event associations.