An Evaluation of Duplicate Adverse Event Reports Characteristics in the Food and Drug Administration Adverse Event Reporting System

Scott Janiczak (), Sarah Tanveer, Karen Tom, Rongmei Zhang, Yong Ma, Lisa Wolf and Monica A. Muñoz
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Scott Janiczak: US Food and Drug Administration
Sarah Tanveer: US Food and Drug Administration
Karen Tom: US Food and Drug Administration
Rongmei Zhang: US Food and Drug Administration
Yong Ma: US Food and Drug Administration
Lisa Wolf: US Food and Drug Administration
Monica A. Muñoz: US Food and Drug Administration

Drug Safety, 2025, vol. 48, issue 10, No 5, 1119-1126

Abstract: Abstract Introduction The Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) receives duplicate reports of adverse events associated with drug and therapeutic biological products. Duplicate reports, defined as multiple reports of the same adverse event(s) related to the administration of the same marketed product(s) to the same individual patient at a particular point in time, may be received in FAERS for many reasons. The presence of duplicate reports can negatively impact public health surveillance efforts by impeding both safety signal identification and signal evaluation. Objectives To characterize the features and contributing factors associated with duplicate reports in FAERS. Methods We manually assessed a convenience sample of individual case safety reports (ICSRs) for duplication, resulting in two data sets: one consisting of non-duplicate reports and one with duplicate reports. We then compared key features of these two datasets, including both structured and unstructured data fields. Key comparison features included: report and reporter type, country of report origin, data source for report, and outcome. In addition, we evaluated information similarity of reports for seven data elements (e.g., age, sex, suspect products) within sets of duplicates using both structured and unstructured fields. We used pairwise sentence bidirectional encoder representations from transformers (SBERT) cosine similarity scores to examine free-text narrative similarity. Results Among the 2297 reports in the sample, 901 (39%) were classified as duplicates, consisting of 237 unique duplicate sets. Compared to non-duplicate reports, duplicates were more likely to be foreign reports (82% versus 37%), reported by healthcare professionals (89% versus 68%), mention other regulatory authority databases (42% versus 11%), describe published case reports (34% versus 11%), or have a serious outcome (97% versus 83%) (p

Date: 2025
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DOI: 10.1007/s40264-025-01560-7

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