Applying quantitative methods for detecting new drug safety signals in pharmacovigilance national database.
OBJECTIVE
To applies three different methods of signal detection to the registered adverse events in Iranian Pharmacovigilance database over the period of 1998-2005.


METHODS
All adverse drug reactions (ADRs) reported to Iranian Pharmacovigilance Center (IPC) from March 1998 through January 2005, were used for the analysis. The data were analysed based on three different signal detection methods including reporting odds ratios (RORs), information component (IC) and proportional reporting ratios (PRRs). The signals detected were categorised based on the number of reports per drug-adverse event combination, severity of the event and labelled or unlabelled ADRs.


RESULTS
During the study period, 6353 cases of ADR reports describing 11 130 reactions were received by IPC. The dataset involved 4975 drug-adverse event combinations. The count of drug-event combinations was 1, 2 and 3 or more for 3470, 726 and 779 combinations, respectively. According to PRRs, there were 2838, 872 and 488 drug-event combinations known as a signal for the pairs with the reporting frequency of 1, 2 and 3 reports, respectively. The results of estimating RORs showed that 2722, 862 and 481 drug-adverse event combinations were detected to be signal for the pairs with the reporting frequency of 1, 2 and 3 reports, respectively, while measuring IC and IC-2SD detected 1120, 378 and 235 for the same reporting frequencies. Diclofenac-induced paralysis and tramadol-induced severe reactions were the most important signals.


CONCLUSIONS
Applying quantitative signal detection methods to the database of national pharmacovigilance centres is necessary to early detection of drug safety alerts.
Shalviri
Gloria
G
School of Public Health and Institute of Public Health Research, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran. shalviri_g@yahoo.com
Mohammad
Kazem
K
Majdzadeh
Reza
R
Gholami
Kheirollah
K
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data*; D006801:Humans