Applying a common data model to Asian databases for multinational pharmacoepidemiologic studies: opportunities and challenges
Received 25 August 2017
Accepted for publication 6 March 2018
Published 27 July 2018 Volume 2018:10 Pages 875—885
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Professor Vera Ehrenstein
Edward Chia-Cheng Lai,1–4 Patrick Ryan,5 Yinghong Zhang,4 Martijn Schuemie,5 N Chantelle Hardy,4 Yukari Kamijima,6 Shinya Kimura,7 Kiyoshi Kubota,6 Kenneth KC Man,8,9 Soo Yeon Cho,10 Rae Woong Park,10 Paul Stang,5 Chien-Chou Su,1,3 Ian CK Wong,8,9 Yea-Huei Yang Kao,1,3 Soko Setoguchi4,11
1School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, National Cheng Kung University, Tainan, Taiwan; 2Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan; 3Health Outcome Research Center, National Cheng-Kung University, Tainan, Taiwan; 4Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA; 5Janssen Research & Development, LLC, Titusville, NJ, USA; 6NPO Drug Safety Research Unit Japan, Tokyo, Japan; 7Japan Medical Data Center Co.,Ltd, Tokyo, Japan; 8Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, University of Hong Kong, Hong Kong, China; 9Research Department of Practice and Policy, UCL School of Pharmacy, London, UK; 10Department of Biomedical Informatics, School of Medicine, Ajou University, Suwon, Korea; 11Institute for Health, Rutgers University and Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
Objective: The goal of the Asian Pharmacoepidemiology Network is to study the effectiveness and safety of medications commonly used in Asia using databases from individual Asian countries. An efficient infrastructure to support multinational pharmacoepidemiologic studies is critical to this effort.
Study design and setting: We converted data from the Japan Medical Data Center database, Taiwan’s National Health Insurance Research Database, Hong Kong’s Clinical Data Analysis and Reporting System, South Korea’s Ajou University School of Medicine database, and the US Medicare 5% sample to the Observational Medical Outcome Partnership common data model (CDM).
Results: We completed and documented the process for the CDM conversion. The coordinating center and participating sites reviewed the documents and refined the conversions based on the comments. The time required to convert data to the CDM varied widely across sites and included conversion to standard terminology codes and refinements of the conversion based on reviews. We mapped 97.2%, 86.7%, 92.6%, and 80.1% of domestic drug codes from the USA, Taiwan, Hong Kong, and Korea to RxNorm, respectively. The mapping rate from Japanese domestic drug codes to RxNorm (70.7%) was lower than from other countries, and we mapped remaining unmapped drugs to Anatomical Therapeutic Chemical Classification System codes. Because the native databases used international procedure coding systems for which mapping tables have been established, we were able to map >90% of diagnosis and procedure codes to standard terminology codes.
Conclusion: The CDM established the foundation and reinforced collaboration for multinational pharmacoepidemiologic studies in Asia. Mapping of terminology codes was the greatest challenge, because of differences in health systems, cultures, and coding systems.
Keywords: clinical coding, computer communication networks, feasibility studies, pharmacoepidemiology, pharmacovigilance
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