{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:15:37Z","timestamp":1753892137266,"version":"3.41.2"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:p><jats:bold>Background\/Objective:<\/jats:bold> Spontaneous reporting systems (SRS) such as the Korea Adverse Event Reporting System (KAERS) are limited in their ability to detect adverse drug reaction (ADR) signals due to their limited data on drug use. Conversely, the national health insurance claim (NHIC) data include drug use information for all qualifying residents. This study aimed to compare ADR signal profiles for antidepressants between KAERS and NHIC, evaluating the extent to which detected signals belong to common ADRs and labeling information.<\/jats:p><jats:p><jats:bold>Materials and Methods:<\/jats:bold> ADR signal detection in KAERS and NHIC databases, spanning January to December 2017, employed disproportionality analysis. Signal classes were determined based on System Organ Class (SOC) of the Medical Dictionary for Regulatory Activities (MedDRA). Also, Common ADR Coverage (CAC), the proportion of detected signals deemed common ADRs, and labeling information coverage (LIC) represented by mean average precision (mAP) were calculated. Additionally, protopathic bias and relative risk (RR) evaluation were performed to check for signal robustness.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> Signal detection revealed 51 and 62 signals in KAERS and NHIC databases, respectively. Both systems predominantly captured signals related to nervous system disorders, comprising 33.3% (N = 17) in KAERS and 50.8% (N = 31) in NHIC. Regarding the type of antidepressants, KAERS predominantly reported signals associated with tricyclic antidepressants (TCAs) (N = 21, 41.2%), while NHIC produced most signals linked to selective serotonin reuptake inhibitors (SSRIs) (N = 22, 35.5%). KAERS exhibited higher CAC (68.63% vs. 29.03%) than NHIC. LIC was also higher in KAERS than in NHIC (mAP for EB05: 1.00 vs. 0.983); i.e., NHIC identified 5 signals not documented in drug labeling information, while KAERS found none. Among the unlabeled signals, one (Duloxetine-Myelopathy) was from protopathic bias, and two (duloxetine-myelopathy and tianeptine-osteomalacia) were statistically significant in RR.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> NHIC exhibited greater capability in detecting ADR signals associated with antidepressant use, encompassing unlabeled ADR signals, compared to KAERS. NHIC also demonstrated greater potential for identifying less common ADRs. Further investigation is needed for signals detected exclusively in NHIC but not covered by labeling information. This study underscores the value of integrating different sources of data, offering substantial regulatory insights and enriching the scope of pharmacovigilance.<\/jats:p>","DOI":"10.3389\/fphar.2023.1291934","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T04:29:05Z","timestamp":1704688145000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing antidepressant safety surveillance: comparative analysis of adverse drug reaction signals in spontaneous reporting and healthcare claims databases"],"prefix":"10.3389","volume":"14","author":[{"given":"Taehyung","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinying","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youran","family":"Noh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryanne","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song Hee","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"B1","first-page":"1","article-title":"PhViD: an R package for pharmacovigilance signal detection","volume":"2014","author":"Ahmed","year":"2013","journal-title":"R. package version"},{"key":"B2","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1002\/pds.1742","article-title":"Quantitative signal detection using spontaneous ADR reporting","volume":"18","author":"Bate","year":"2009","journal-title":"Pharmacoepidemiol. drug Saf."},{"key":"B3","doi-asserted-by":"publisher","first-page":"109","DOI":"10.2165\/00002018-199920020-00002","article-title":"The medical dictionary for regulatory activities (MedDRA)","volume":"20","author":"Brown","year":"1999","journal-title":"Drug Saf."},{"key":"B4","doi-asserted-by":"publisher","first-page":"499","DOI":"10.32614\/rj-2017-063","article-title":"openEBGM: an R implementation of the gamma-Poisson shrinker data mining model","volume":"9","author":"Canida","year":"2017","journal-title":"R. 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