Geetha Iyer1, Sathiya Priya Marimuthu2,3, Jodi B Segal2,4,5, Sonal Singh6. 1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA. 2. Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 3. Division of Clinical Pharmacology, Johns Hopkins School of Medicine, Baltimore, MD, USA. 4. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 5. Division of General Internal Medicine, Department of Medicine, Johns Hopkins Medicine, Baltimore, MD, USA. 6. Department of Family Medicine and Community Health and Meyers Primary Care Institute, Umass Memorial Health Care, University of Massachusetts Medical School, 55 Lake Ave North, Worcester, MA, 01655-0002, USA. Sonal.Singh@umassmemorial.org.
Abstract
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.
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.
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