An Algorithm to Identify Generic Drugs in the FDA Adverse Event Reporting System

• Published in: Drug safety Vol. 40; no. 9; pp. 799 - 808
• Main Authors: Iyer, Geetha, Marimuthu, Sathiya Priya, Segal, Jodi B., Singh, Sonal
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2017; Springer Nature B.V
• Subjects: Adverse Drug Reaction Reporting Systems - statistics & numerical data; Algorithms; Amphetamines; Case reports; Confidence intervals; Consistency; Criteria; Data processing; Databases, Factual; Drug abuse; Drug Safety and Pharmacovigilance; Drug-Related Side Effects and Adverse Reactions - epidemiology; Drugs; Drugs, Generic - administration & dosage; Drugs, Generic - adverse effects; Food; Generic drugs; Humans; Identification methods; International conferences; Medical errors; Medicine; Medicine & Public Health; Names; Observer Variation; Original Research Article; Patients; Pharmacology/Toxicology; Prescription drugs; Product safety; Reliability analysis; Reproducibility of Results; Surveillance; Systematic review; Thyroxine; United States - epidemiology; United States Food and Drug Administration; United States > US
• ISSN: 0114-5916; 1179-1942; 1179-1942
• DOI: 10.1007/s40264-017-0550-1

Introduction Although generic drugs constitute approximately 88% of drugs prescribed in the US, there are no reliable methods to identify generic drugs in the US FDA Adverse Event Reporting System (FAERS). Objective The aim of this study was to develop an algorithm for identifying generic drugs in the FAERS. Data Source We used 1237 adverse event reports for tamsulosin, levothyroxine, and amphetamine/dextroamphetamine from the publicly available FAERS from 2011–2013, and 277 source case narratives obtained from the FDA. Methods Two reviewers independently and in duplicate used a three-item algorithm including the following criteria: manufacturer name, New Drug Application (NDA) number/abbreviated NDA (ANDA), and specific use of the term ‘generic’ or ‘brand’ to classify the focal drug of each case report as definitely generic (two of three criteria), probably generic (one of three criteria), brand , and cannot be assessed . Inter-rater reliability was estimated using kappa coefficients, and internal consistency was estimated using Cronbach’s alpha. We compared the classification of the drugs as generic versus non-generic in publicly available FAERS compared with the original case reports (reference). Results The focal drug was classified as generic (definite or probable) in 15.8% (39/234), 9% (67/742), and 16.7% (42/261) of tamsulosin, levothyroxine and amphetamine/dextroamphetamine cases, respectively (overall kappa 0.89, 95% confidence interval 0.85–0.93), while 37% of reports could not be classified due to incomplete information. Among the drugs classified as generics using the publicly available FAERS, we categorized 95.3% as generic drugs using the original case reports. Among those drugs that did not meet the algorithm-based definition of generic in the publicly available data, 20.9% were reclassified as generics using the original case reports. Conclusions The algorithm demonstrated high inter-rater reliability with moderate internal consistency for identifying generic drugs in the FAERS, in our sample. Future efforts should focus on improving the reliability and validity of identifying generics through improving the completeness of reporting in the FAERS.