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Chan AYL, Gao L, Hsieh MHC, Kjerpeseth LJ, Avelar R, Banaschewski T, Chan AHY, Coghill D, Cohen JM, Gissler M, Harrison J, Ip P, Karlstad Ø, Lau WCY, Leinonen MK, Leung WC, Liao TC, Reutfors J, Shao SC, Simonoff E, Tan KCB, Taxis K, Tomlin A, Cesta CE, Lai ECC, Zoega H, Man KKC, Wong ICK. Maternal diabetes and risk of attention-deficit/hyperactivity disorder in offspring in a multinational cohort of 3.6 million mother-child pairs. Nat Med 2024; 30:1416-1423. [PMID: 38589601 PMCID: PMC11108779 DOI: 10.1038/s41591-024-02917-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/08/2024] [Indexed: 04/10/2024]
Abstract
Previous studies report an association between maternal diabetes mellitus (MDM) and attention-deficit/hyperactivity disorder (ADHD), often overlooking unmeasured confounders such as shared genetics and environmental factors. We therefore conducted a multinational cohort study with linked mother-child pairs data in Hong Kong, New Zealand, Taiwan, Finland, Iceland, Norway and Sweden to evaluate associations between different MDM (any MDM, gestational diabetes mellitus (GDM) and pregestational diabetes mellitus (PGDM)) and ADHD using Cox proportional hazards regression. We included over 3.6 million mother-child pairs between 2001 and 2014 with follow-up until 2020. Children who were born to mothers with any type of diabetes during pregnancy had a higher risk of ADHD than unexposed children (pooled hazard ratio (HR) = 1.16, 95% confidence interval (CI) = 1.08-1.24). Higher risks of ADHD were also observed for both GDM (pooled HR = 1.10, 95% CI = 1.04-1.17) and PGDM (pooled HR = 1.39, 95% CI = 1.25-1.55). However, siblings with discordant exposure to GDM in pregnancy had similar risks of ADHD (pooled HR = 1.05, 95% CI = 0.94-1.17), suggesting potential confounding by unmeasured, shared familial factors. Our findings indicate that there is a small-to-moderate association between MDM and ADHD, whereas the association between GDM and ADHD is unlikely to be causal. This finding contrast with previous studies, which reported substantially higher risk estimates, and underscores the need to reevaluate the precise roles of hyperglycemia and genetic factors in the relationship between MDM and ADHD.
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Affiliation(s)
- Adrienne Y L Chan
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Pak Shek Kok, Hong Kong
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
- Groningen Research Institute of Pharmacy, Unit of PharmacoTherapy, Epidemiology and Economics, University of Groningen, Groningen, The Netherlands
| | - Le Gao
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | - Miyuki Hsing-Chun Hsieh
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Lars J Kjerpeseth
- Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway
| | - Raquel Avelar
- Institute of Biological Psychiatry, Mental Health Centre Sct Hans, Mental Health Services, Copenhagen, Denmark
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Amy Hai Yan Chan
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - David Coghill
- Departments of Paediatrics and Psychiatry, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Jacqueline M Cohen
- Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Mika Gissler
- Centre for Pharmacoepidemiology, Department of Medicine, Karolinska Institutet, Solna, Sweden
- Academic Primary Health Care Centre, Region Stockholm, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Research Centre for Child Psychiatry, University of Turku, Turku, Finland
| | - Jeff Harrison
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Patrick Ip
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Øystein Karlstad
- Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway
| | - Wallis C Y Lau
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Pak Shek Kok, Hong Kong
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
| | - Maarit K Leinonen
- Knowledge Brokers, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Wing Cheong Leung
- Department of Obstetrics and Gynaecology, Kwong Wah Hospital, Yau Ma Tei, Hong Kong
| | - Tzu-Chi Liao
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Johan Reutfors
- Centre for Pharmacoepidemiology, Department of Medicine, Karolinska Institutet, Solna, Sweden
| | - Shih-Chieh Shao
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Pharmacy, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Emily Simonoff
- Department of Child and Adolescent Psychiatry, King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Kathryn Choon Beng Tan
- Department of Medicine, School of Clinical Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | - Katja Taxis
- Groningen Research Institute of Pharmacy, Unit of PharmacoTherapy, Epidemiology and Economics, University of Groningen, Groningen, The Netherlands
| | - Andrew Tomlin
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Carolyn E Cesta
- Centre for Pharmacoepidemiology, Department of Medicine, Karolinska Institutet, Solna, Sweden.
| | - Edward Chia-Cheng Lai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
| | - Helga Zoega
- Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
- School of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
| | - Kenneth K C Man
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong.
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Pak Shek Kok, Hong Kong.
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK.
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK.
| | - Ian C K Wong
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong.
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Pak Shek Kok, Hong Kong.
- School of Pharmacy, Medical Sciences Division, Macau University of Science and Technology, Taipa, Macau.
- Advance Data Analytics for Medical Science Limited, Hong Kong, Hong Kong.
- School of Pharmacy, Aston University, Birmingham, UK.
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Chai Y, Man KKC, Luo H, Torre CO, Wing YK, Hayes JF, Osborn DPJ, Chang WC, Lin X, Yin C, Chan EW, Lam ICH, Fortin S, Kern DM, Lee DY, Park RW, Jang JW, Li J, Seager S, Lau WCY, Wong ICK. Incidence of mental health diagnoses during the COVID-19 pandemic: a multinational network study. Epidemiol Psychiatr Sci 2024; 33:e9. [PMID: 38433286 PMCID: PMC10940053 DOI: 10.1017/s2045796024000088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 12/27/2023] [Accepted: 01/20/2024] [Indexed: 03/05/2024] Open
Abstract
AIMS Population-wide restrictions during the COVID-19 pandemic may create barriers to mental health diagnosis. This study aims to examine changes in the number of incident cases and the incidence rates of mental health diagnoses during the COVID-19 pandemic. METHODS By using electronic health records from France, Germany, Italy, South Korea and the UK and claims data from the US, this study conducted interrupted time-series analyses to compare the monthly incident cases and the incidence of depressive disorders, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, personality disorders and psychoses diagnoses before (January 2017 to February 2020) and after (April 2020 to the latest available date of each database [up to November 2021]) the introduction of COVID-related restrictions. RESULTS A total of 629,712,954 individuals were enrolled across nine databases. Following the introduction of restrictions, an immediate decline was observed in the number of incident cases of all mental health diagnoses in the US (rate ratios (RRs) ranged from 0.005 to 0.677) and in the incidence of all conditions in France, Germany, Italy and the US (RRs ranged from 0.002 to 0.422). In the UK, significant reductions were only observed in common mental illnesses. The number of incident cases and the incidence began to return to or exceed pre-pandemic levels in most countries from mid-2020 through 2021. CONCLUSIONS Healthcare providers should be prepared to deliver service adaptations to mitigate burdens directly or indirectly caused by delays in the diagnosis and treatment of mental health conditions.
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Affiliation(s)
- Yi Chai
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- The Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong
| | - Kenneth K. C. Man
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Hong Kong
| | - Hao Luo
- The Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong
- Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong
- Sau Po Centre on Ageing, The University of Hong Kong, Hong Kong
| | - Carmen Olga Torre
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Real World Data Sciences, Roche, Welwyn Garden City, UK
- School of Science and Engineering, University of Groningen, Groningen, The Netherlands
| | - Yun Kwok Wing
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - Joseph F. Hayes
- Division of Psychiatry, University College London, London, UK
- Camden and Islington NHS Foundation Trust, London, UK
| | - David P. J. Osborn
- Division of Psychiatry, University College London, London, UK
- Camden and Islington NHS Foundation Trust, London, UK
| | - Wing Chung Chang
- Department of Psychiatry, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong
| | - Xiaoyu Lin
- Real-World Solutions, IQVIA, Durham, NC, USA
| | - Can Yin
- Real-World Solutions, IQVIA, Durham, NC, USA
| | - Esther W. Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Hong Kong
- The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, Guangdong, China
| | - Ivan C. H. Lam
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Stephen Fortin
- Observation Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA
| | - David M. Kern
- Department of Epidemiology, Janssen Research & Development, Titusville, NJ, USA
| | - Dong Yun Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, South Korea
| | - Jing Li
- Real-World Solutions, IQVIA, Durham, NC, USA
| | | | - Wallis C. Y. Lau
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Hong Kong
| | - Ian C. K. Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
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Tsai DHT, Bell JS, Abtahi S, Baak BN, Bazelier MT, Brauer R, Chan AYL, Chan EW, Chen H, Chui CSL, Cook S, Crystal S, Gandhi P, Hartikainen S, Ho FK, Hsu ST, Ilomäki J, Kim JH, Klungel OH, Koponen M, Lau WCY, Lau KK, Lum TYS, Luo H, Man KKC, Pell JP, Setoguchi S, Shao SC, Shen CY, Shin JY, Souverein PC, Tolppanen AM, Wei L, Wong ICK, Lai ECC. Cross-Regional Data Initiative for the Assessment and Development of Treatment for Neurological and Mental Disorders. Clin Epidemiol 2023; 15:1241-1252. [PMID: 38146486 PMCID: PMC10749544 DOI: 10.2147/clep.s426485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/04/2023] [Indexed: 12/27/2023] Open
Abstract
Purpose To describe and categorize detailed components of databases in the Neurological and Mental Health Global Epidemiology Network (NeuroGEN). Methods An online 132-item questionnaire was sent to key researchers and data custodians of NeuroGEN in North America, Europe, Asia and Oceania. From the responses, we assessed data characteristics including population coverage, data follow-up, clinical information, validity of diagnoses, medication use and data latency. We also evaluated the possibility of conversion into a common data model (CDM) to implement a federated network approach. Moreover, we used radar charts to visualize the data capacity assessments, based on different perspectives. Results The results indicated that the 15 databases covered approximately 320 million individuals, included in 7 nationwide claims databases from Australia, Finland, South Korea, Taiwan and the US, 6 population-based electronic health record databases from Hong Kong, Scotland, Taiwan, the Netherlands and the UK, and 2 biomedical databases from Taiwan and the UK. Conclusion The 15 databases showed good potential for a federated network approach using a common data model. Our study provided publicly accessible information on these databases for those seeking to employ real-world data to facilitate current assessment and future development of treatments for neurological and mental disorders.
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Affiliation(s)
- Daniel Hsiang-Te Tsai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Centre for Neonatal and Paediatric Infection, St George’s University of London, London, UK
| | - J Simon Bell
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
| | - Shahab Abtahi
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands
| | - Brenda N Baak
- PHARMO Institute for Drug Outcomes Research, Utrecht, the Netherlands
| | - Marloes T Bazelier
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands
| | - Ruth Brauer
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
| | - Adrienne Y L Chan
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
- Groningen Research Institute of Pharmacy, Unit of Pharmacotherapy, ‐Epidemiology and ‐Economics, University of Groningen, Groningen, the Netherlands
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Hong Kong, Special Administrative Region, People’s Republic of China
| | - Esther W Chan
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Hong Kong, Special Administrative Region, People’s Republic of China
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
- Department of Pharmacy, the University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China
- The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, People’s Republic of China
| | - Haoqian Chen
- Center for Pharmacoepidemiology and Treatment Science (PETS), Institute for Health, Rutgers University, New Brunswick, NJ, USA
| | - Celine S L Chui
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Hong Kong, Special Administrative Region, People’s Republic of China
- School of Nursing, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
| | - Sharon Cook
- Center for Health Services Research, Rutgers University, New Brunswick, NJ, USA
| | - Stephen Crystal
- Center for Health Services Research, Rutgers University, New Brunswick, NJ, USA
| | - Poonam Gandhi
- Center for Pharmacoepidemiology and Treatment Science (PETS), Institute for Health, Rutgers University, New Brunswick, NJ, USA
| | - Sirpa Hartikainen
- Kuopio Research Centre of Geriatric Care and School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Frederick K Ho
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Shao-Ti Hsu
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Jenni Ilomäki
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
| | - Ju Hwan Kim
- School of Pharmacy, Sungkyunkwan University, Suwon, South Korea
| | - Olaf H Klungel
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands
| | - Marjaana Koponen
- Kuopio Research Centre of Geriatric Care and School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Wallis C Y Lau
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Hong Kong, Special Administrative Region, People’s Republic of China
| | - Kui Kai Lau
- Division of Neurology, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
- State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
| | - Terry Y S Lum
- Department of Social Work and Social Administration, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
| | - Hao Luo
- Department of Social Work and Social Administration, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
| | - Kenneth K C Man
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Hong Kong, Special Administrative Region, People’s Republic of China
| | - Jill P Pell
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Soko Setoguchi
- Center for Pharmacoepidemiology and Treatment Science (PETS), Institute for Health, Rutgers University, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School and Pharmacoepidemiology and Treatments Science, Institute for Health, Rutgers University, New Brunswick, NJ, USA
| | - Shih-Chieh Shao
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Pharmacy, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Chin-Yao Shen
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ju-Young Shin
- School of Pharmacy, Sungkyunkwan University, Suwon, South Korea
- Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea
- Department of Biohealth Regulatory Science, Sungkyunkwan University, Seoul, South Korea
| | - Patrick C Souverein
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands
| | - Anna-Maija Tolppanen
- Kuopio Research Centre of Geriatric Care and School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Li Wei
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Hong Kong, Special Administrative Region, People’s Republic of China
| | - Ian C K Wong
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, Hong Kong, Special Administrative Region, People’s Republic of China
- Aston Pharmacy School, Aston University, Birmingham, UK
| | - Edward Chia-Cheng Lai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
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Zhong J, Zhang J, Fang H, Liu L, Xie J, Wu E. Advancing the development of real-world data for healthcare research in China: challenges and opportunities. BMJ Open 2022; 12:e063139. [PMID: 35906059 PMCID: PMC9345036 DOI: 10.1136/bmjopen-2022-063139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Various real-world data (RWD) sources have emerged in China with the intention of generating real-world evidence (RWE) that can be used in clinical and regulatory decision-making. Despite these efforts, significant barriers remain that hinder high-quality healthcare research. A workshop with 30 representatives from healthcare research agencies, technology companies focused on healthcare big data and pharmaceutical companies was held in December 2020 to identify strategies to overcome the barriers associated with the usability and quality of RWD in China. Across all sectors, examples of barriers identified included inconsistencies in terminology and non-standardised coding practices; the absence of longitudinal data; the absence of transparent data processing and validation practices; and the inability to access and share RWD. While cutting-edge technological innovations and data solutions provided powerful tools, the development of collaborative and synergistic research networks across multiple stakeholders is key to generate accessible, high-quality RWD in China. RWD has the potential to provide clinical, regulatory and reimbursement decision-makers with critical insights that can improve healthcare delivery in China. However, barriers to its access, collection and use must be addressed to generate RWE to guide healthcare stakeholders.
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Affiliation(s)
| | - Jun Zhang
- MSD R&D (China) Co., Ltd, Beijing, China
| | | | - Larry Liu
- Merck & Co., Inc, Rahway, New Jersey, USA
- Weill Cornell Medical College, New York, New York, USA
| | - Jipan Xie
- Analysis Group, Inc, Los Angeles, California, USA
| | - Eric Wu
- Analysis Group, Inc, Boston, Massachusetts, USA
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5
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Quiroz JC, Chard T, Sa Z, Ritchie A, Jorm L, Gallego B. Extract, transform, load framework for the conversion of health databases to OMOP. PLoS One 2022; 17:e0266911. [PMID: 35404974 PMCID: PMC9000122 DOI: 10.1371/journal.pone.0266911] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/29/2022] [Indexed: 11/22/2022] Open
Abstract
Common data models standardize the structures and semantics of health datasets, enabling reproducibility and large-scale studies that leverage the data from multiple locations and settings. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) is one of the leading common data models. While there is a strong incentive to convert datasets to OMOP, the conversion is time and resource-intensive, leaving the research community in need of tools for mapping data to OMOP. We propose an extract, transform, load (ETL) framework that is metadata-driven and generic across source datasets. The ETL framework uses a new data manipulation language (DML) that organizes SQL snippets in YAML. Our framework includes a compiler that converts YAML files with mapping logic into an ETL script. Access to the ETL framework is available via a web application, allowing users to upload and edit YAML files via web editor and obtain an ETL SQL script for use in development environments. The structure of the DML maximizes readability, refactoring, and maintainability, while minimizing technical debt and standardizing the writing of ETL operations for mapping to OMOP. Our framework also supports transparency of the mapping process and reuse by different institutions.
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Affiliation(s)
- Juan C. Quiroz
- Centre for Big Data Research in Health, UNSW, Sydney, Australia
- * E-mail:
| | - Tim Chard
- Centre for Big Data Research in Health, UNSW, Sydney, Australia
| | - Zhisheng Sa
- Centre for Big Data Research in Health, UNSW, Sydney, Australia
| | - Angus Ritchie
- Concord Clinical School, University of Sydney, Sydney, Australia
- Health Informatics Unit, Sydney Local Health District, Camperdown, Australia
| | - Louisa Jorm
- Centre for Big Data Research in Health, UNSW, Sydney, Australia
| | - Blanca Gallego
- Centre for Big Data Research in Health, UNSW, Sydney, Australia
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6
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Kim TH, Noh S, Kim YR, Lee C, Kim JE, Jeong CW, Yoon KH. Development and validation of a management system and dataset quality assessment tool for the Radiology Common Data Model (R_CDM): A case study in liver disease. Int J Med Inform 2022; 162:104759. [PMID: 35390589 DOI: 10.1016/j.ijmedinf.2022.104759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 03/17/2022] [Accepted: 03/29/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND The Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM), a distributed research network, has low clinical data coverage. Radiological data are valuable, but imaging metadata are often incomplete, and a standardized recording format in the OMOP-CDM is lacking. We developed a web-based management system and data quality assessment (RQA) tool for a radiology_CDM (R_CDM) and evaluated the feasibility of clinically applying this dataset. METHODS We designed an R_CDM with Radiology_Occurrence and Radiology_Image tables. This was seamlessly linked to the OMOP-CDM clinical data. We adopted the standardized terminology using the RadLex playbook and mapped 5,753 radiology protocol terms to the OMOP vocabulary. An extract, transform, and load (ETL) process was developed to extract detailed information that was difficult to extract from metadata and to compensate for missing values. Image-based quantification was performed to measure liver surface nodularity (LSN), using customized Wonkwang abdomen and liver total solution (WALTS) software. RESULTS On a PACS, 368,333,676 DICOM files (1,001,797 cases) were converted to R_CDM chronic liver disease (CLD) data (316,596 MR images, 228 cases; 926,753 CT images, 782 cases) and uploaded to a web-based management system. Acquisition date and resolution were extracted accurately, but other information, such as "contrast administration status" and "photography direction", could not be extracted from the metadata. Using WALTS, 9,609 pre-contrast axial-plane abdominal MR images (197 CLD cases) were assigned LSN scores by METAVIR fibrosis grades, which differed significantly by ANOVA (p < 0.001). The mean RQA score (83.5) indicated good quality. CONCLUSION This study developed a web-based system for management of the R_CDM dataset, RQA tool, and constructed a CLD R_CDM dataset, with good quality for clinical application. Our management system and R_CDM CLD dataset would be useful for multicentric and image-based quantification researches.
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Affiliation(s)
- Tae-Hoon Kim
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea
| | - SiHyeong Noh
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea
| | - Youe Ree Kim
- Department of Radiology, Wonkwang University School of Medicine and Wonkwang University Hospital, Iksan 54538, Republic of Korea
| | - ChungSub Lee
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea
| | - Ji Eon Kim
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea
| | - Chang-Won Jeong
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea.
| | - Kwon-Ha Yoon
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea; Department of Radiology, Wonkwang University School of Medicine and Wonkwang University Hospital, Iksan 54538, Republic of Korea.
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7
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Kang DY, Kim H, Ko S, Kim H, Shinn J, Kang MG, Byeon SJ, Choi JH, Shin SY, Kim HS. Sodium-Glucose Cotransporter-2 Inhibitor-Related Diabetic Ketoacidosis: Accuracy Verification of Operational Definition. J Korean Med Sci 2022; 37:e53. [PMID: 35191230 PMCID: PMC8860766 DOI: 10.3346/jkms.2022.37.e53] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/05/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND The most important aspect of a retrospective cohort study is the operational definition (OP) of the disease. We developed a detailed OP for the detection of sodium-glucose cotransporter-2 inhibitors (SGLT2i) related to diabetic ketoacidosis (DKA). The OP was systemically verified and analyzed. METHODS All patients prescribed SGLT2i at four university hospitals were enrolled in this experiment. A DKA diagnostic algorithm was created and distributed to each hospital; subsequently, the number of SGLT2i-related DKAs was confirmed. Then, the algorithm functionality was verified through manual chart reviews by an endocrinologist using the same OP. RESULTS A total of 8,958 patients were initially prescribed SGLT2i. According to the algorithm, 0.18% (16/8,958) were confirmed to have SGLT2i-related DKA. However, based on manual chart reviews of these 16 cases, there was only one case of SGLT2i-related DKA (positive predictive value = 6.3%). Even after repeatedly narrowing the diagnosis range of the algorithm, the effect of a positive predictive value was insignificant (6.3-10.0%, P > 0.999). CONCLUSION Owing to the nature of electronic medical record data, we could not create an algorithm that clearly differentiates SGLT2i-related DKA despite repeated attempts. In all retrospective studies, a portion of the samples should be randomly selected to confirm the accuracy of the OP through chart review. In retrospective cohort studies in which chart review is not possible, it will be difficult to guarantee the reliability of the results.
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Affiliation(s)
- Dong Yoon Kang
- Drug Safety Monitoring Center, Seoul National University Hospital, Seoul, Korea
| | - Hyunah Kim
- College of Pharmacy, Sookmyung Women's University, Seoul, Korea
| | - SooJeong Ko
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - HyungMin Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jiwon Shinn
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Min-Gyu Kang
- Division of Allergy and Clinical Immunology, Departmemt of Internal Medicine, Chungbuk National University Hospital, Chungbuk National College of Medicine, Cheongju, Korea
| | - Sun-Ju Byeon
- Department of Pathology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea
| | - Jeong-Hee Choi
- Department of Pulmonology and Allergy, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea
| | - Soo-Yong Shin
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Hun-Sung Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
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8
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Man KKC, Shao SC, Chaiyakunapruk N, Dilokthornsakul P, Kubota K, Li J, Ooba N, Pratt N, Pottegård A, Rasmussen L, Roughead EE, Shin JY, Su CC, Wong ICK, Kao Yang YH, Lai ECC. Metabolic events associated with the use of antipsychotics in children, adolescents and young adults: a multinational sequence symmetry study. Eur Child Adolesc Psychiatry 2022; 31:99-120. [PMID: 33185773 DOI: 10.1007/s00787-020-01674-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 10/23/2020] [Indexed: 12/15/2022]
Abstract
It is known that younger patients treated with antipsychotics are at increased risk of metabolic events; however, it is unknown how this risk varies according to ethnicity, the class of antipsychotic and the specific product used, and by age group. We conducted a multinational sequence symmetry study in Asian populations (Hong Kong, Japan, Korea, Taiwan and Thailand) and non-Asian populations (Australia and Denmark) to evaluate the metabolic events associated with antipsychotics in both Asian and non-Asian populations, for typical and atypical antipsychotics, and by the subgroups of children and adolescents, and young adults. Patients aged 6-30 years newly initiating oral antipsychotic drugs were included. We defined a composite outcome for metabolic events which included dyslipidemia, hypertension and hyperglycemia. We calculated the sequence ratio (SR) by dividing the number of people for whom a medicine for one of the outcome events was initiated within a 12-month period after antipsychotic initiation by the number before antipsychotic initiation. This study included 346,904 antipsychotic initiators across seven countries. Antipsychotic use was associated with an increased risk of composite metabolic events with a pooled adjusted SR (ASR) of 1.22 (95% CI 1.00-1.50). Pooled ASRs were similar between Asian (ASR, 1.22; 95% CI 0.88-1.70) and non-Asian populations (ASR, 1.22; 95% CI 1.04-1.43). The pooled ASR for typical and atypical antipsychotics was 0.98 (95% CI 0.85-1.12) and 1.24 (95% CI 0.97-1.59), respectively. No difference was observed in the relative effect in children and adolescents compared to young adults. The risk of metabolic events associated with antipsychotics use was similar in magnitude in Asian and non-Asian populations despite the marked difference in drug utilization patterns.
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Affiliation(s)
- Kenneth K C Man
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China
| | - Shih-Chieh Shao
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan, 701, Taiwan
- Department of Pharmacy, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Nathorn Chaiyakunapruk
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, 30 2000 E, Salt Lake City, UT, 84112, USA
- School of Pharmacy, Monash University Malaysia, Selangor, Malaysia
| | - Piyameth Dilokthornsakul
- Center of Pharmaceutical Outcomes Research, Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok, Thailand
| | | | - Junqing Li
- School of Pharmacy, Sungkyunkwan University, Seoul, South Korea
| | - Nobuhiro Ooba
- Department of Clinical Pharmacy, Nihon University School of Pharmacy, Chiba, Japan
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Anton Pottegård
- Clinical Pharmacology and Pharmacy, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Lotte Rasmussen
- Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan
| | - Elizabeth E Roughead
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Ju-Young Shin
- School of Pharmacy, Sungkyunkwan University, Seoul, South Korea
| | - Chien-Chou Su
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan, 701, Taiwan
- Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan
| | - Ian C K Wong
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China
| | - Yea-Huei Kao Yang
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan, 701, Taiwan
| | - Edward Chia-Cheng Lai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, No.1, University Road, Tainan, 701, Taiwan.
- Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan.
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9
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Cardiovascular and metabolic risk of antipsychotics in children and young adults: a multinational self-controlled case series study. Epidemiol Psychiatr Sci 2021; 30:e65. [PMID: 34751642 PMCID: PMC8546502 DOI: 10.1017/s2045796021000494] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
AIMS The risk of antipsychotic-associated cardiovascular and metabolic events may differ among countries, and limited real-world evidence has been available comparing the corresponding risks among children and young adults. We, therefore, evaluated the risks of cardiovascular and metabolic events in children and young adults receiving antipsychotics. METHODS We conducted a multinational self-controlled case series (SCCS) study and included patients aged 6-30 years old who had both exposure to antipsychotics and study outcomes from four nationwide databases of Taiwan (2004-2012), Korea (2010-2016), Hong Kong (2001-2014) and the UK (1997-2016) that covers a total of approximately 100 million individuals. We investigated three antipsychotics exposure windows (i.e., 90 days pre-exposure, 1-30 days, 30-90 days and 90 + days of exposure). The outcomes were cardiovascular events (stroke, ischaemic heart disease and acute myocardial infarction), or metabolic events (hypertension, type 2 diabetes mellitus and dyslipidaemia). RESULTS We included a total of 48 515 individuals in the SCCS analysis. We found an increased risk of metabolic events only in the risk window with more than 90-day exposure, with a pooled IRR of 1.29 (95% CI 1.20-1.38). The pooled IRR was 0.98 (0.90-1.06) for 1-30 days and 0.88 (0.76-1.02) for 31-90 days. We found no association in any exposure window for cardiovascular events. The pooled IRR was 1.86 (0.74-4.64) for 1-30 days, 1.35 (0.74-2.47) for 31-90 days and 1.29 (0.98-1.70) for 90 + days. CONCLUSIONS Long-term exposure to antipsychotics was associated with an increased risk of metabolic events but did not trigger cardiovascular events in children and young adults.
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10
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Sing CW, Lin TC, Bartholomew S, Bell JS, Bennett C, Beyene K, Bosco-Lévy P, Chan AHY, Chandran M, Cheung CL, Doyon CY, Droz-Perroteau C, Ganesan G, Hartikainen S, Ilomaki J, Jeong HE, Kiel DP, Kubota K, Lai ECC, Lange J, Lewiecki EM, Liu J, Man KKC, Mendes de Abreu M, Moore N, O'Kelly J, Ooba N, Pedersen AB, Prieto-Alhambra D, Shin JY, Sørensen HT, Tan KB, Tolppanen AM, Verhamme KMC, Wang GHM, Watcharathanakij S, Zhao H, Wong ICK. Global epidemiology of hip fractures: a study protocol using a common analytical platform among multiple countries. BMJ Open 2021; 11:e047258. [PMID: 34321298 PMCID: PMC8319985 DOI: 10.1136/bmjopen-2020-047258] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
INTRODUCTION Hip fractures are associated with a high burden of morbidity and mortality. Globally, there is wide variation in the incidence of hip fracture in people aged 50 years and older. Longitudinal and cross-geographical comparisons of health data can provide insights on aetiology, risk factors, and healthcare practices. However, systematic reviews of studies that use different methods and study periods do not permit direct comparison across geographical regions. Thus, the objective of this study is to investigate global secular trends in hip fracture incidence, mortality and use of postfracture pharmacological treatment across Asia, Oceania, North and South America, and Western and Northern Europe using a unified methodology applied to health records. METHODS AND ANALYSIS This retrospective cohort study will use a common protocol and an analytical common data model approach to examine incidence of hip fracture across population-based databases in different geographical regions and healthcare settings. The study period will be from 2005 to 2018 subject to data availability in study sites. Patients aged 50 years and older and hospitalised due to hip fracture during the study period will be included. The primary outcome will be expressed as the annual incidence of hip fracture. Secondary outcomes will be the pharmacological treatment rate and mortality within 12 months following initial hip fracture by year. For the primary outcome, crude and standardised incidence of hip fracture will be reported. Linear regression will be used to test for time trends in the annual incidence. For secondary outcomes, the crude mortality and standardised mortality incidence will be reported. ETHICS AND DISSEMINATION Each participating site will follow the relevant local ethics and regulatory frameworks for study approval. The results of the study will be submitted for peer-reviewed scientific publications and presented at scientific conferences.
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Affiliation(s)
- Chor-Wing Sing
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR
| | - Tzu-Chieh Lin
- Center for Observational Research, Amgen Inc, Thousand Oaks, California, USA
| | - Sharon Bartholomew
- Centre for Surveillance and Applied Research, Public Health Agency of Canada, Ottawa, Ontario, Canada
| | - J Simon Bell
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia
| | - Corina Bennett
- Center for Observational Research, Amgen Inc, Thousand Oaks, California, USA
| | - Kebede Beyene
- School of Pharmacy, The University of Auckland, Auckland, New Zealand
| | | | - Amy Hai Yan Chan
- School of Pharmacy, The University of Auckland, Auckland, New Zealand
| | - Manju Chandran
- Osteoporosis and Bone Metabolism Unit, Department of Endocrinology, Singapore General Hospital, Singapore
| | - Ching-Lung Cheung
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR
| | - Caroline Y Doyon
- Centre for Surveillance and Applied Research, Public Health Agency of Canada, Ottawa, Ontario, Canada
| | | | | | | | - Jenni Ilomaki
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia
| | - Han Eol Jeong
- School of Pharmacy, Sungkyunkwan University, Suwon, South Korea
| | - Douglas P Kiel
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife and Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Edward Chia-Cheng Lai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, National Cheng Kung University, Tainan, Taiwan
| | - Jeff Lange
- Center for Observational Research, Amgen Inc, Thousand Oaks, California, USA
| | - E Michael Lewiecki
- University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
| | - Jiannong Liu
- Chronic Disease Research Group, Hennepin Healthcare Research Institute, Minneapolis, Minnesota, USA
| | - Kenneth K C Man
- Research Department of Practice and Policy, University College London School of Pharmacy, London, UK
- Centre for Medicines Optimisation Research and Education (CMORE), University College London Hospital, London, UK
| | - Mirhelen Mendes de Abreu
- Rheumatology Service, Internal Medicine Department, School of Medicine, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Nicolas Moore
- Bordeaux PharmacoEpi, University of Bordeaux, Bordeaux, France
| | - James O'Kelly
- Center for Observational Research, Amgen Inc, Thousand Oaks, California, USA
| | - Nobuhiro Ooba
- School of Pharmacy, The Nihon University, Chiba, Japan
| | - Alma B Pedersen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus University, Aarhus, Denmark
| | - Daniel Prieto-Alhambra
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Ju-Young Shin
- School of Pharmacy, Sungkyunkwan University, Suwon, South Korea
| | - Henrik T Sørensen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus University, Aarhus, Denmark
| | - Kelvin Bryan Tan
- Ministry of Health Singapore, Singapore
- School of Public Health, National University of Singapore, Singapore
| | | | - Katia M C Verhamme
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, Netherlands
| | - Grace Hsin-Min Wang
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, National Cheng Kung University, Tainan, Taiwan
| | - Sawaeng Watcharathanakij
- Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Mueang Si Khai, Ubon Ratchathani, Thailand
| | - Hongxin Zhao
- Shanghai Synyi Medical Technology Co Ltd, Shanghai, China
| | - Ian C K Wong
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR
- Research Department of Practice and Policy, University College London School of Pharmacy, London, UK
- Centre for Medicines Optimisation Research and Education (CMORE), University College London Hospital, London, UK
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Transforming electronic health record polysomnographic data into the Observational Medical Outcome Partnership's Common Data Model: a pilot feasibility study. Sci Rep 2021; 11:7013. [PMID: 33782494 PMCID: PMC8007756 DOI: 10.1038/s41598-021-86564-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/11/2021] [Indexed: 12/11/2022] Open
Abstract
Well-defined large-volume polysomnographic (PSG) data can identify subgroups and predict outcomes of obstructive sleep apnea (OSA). However, current PSG data are scattered across numerous sleep laboratories and have different formats in the electronic health record (EHR). Hence, this study aimed to convert EHR PSG into a standardized data format-the Observational Medical Outcome Partnership (OMOP) common data model (CDM). We extracted the PSG data of a university hospital for the period from 2004 to 2019. We designed and implemented an extract-transform-load (ETL) process to transform PSG data into the OMOP CDM format and verified the data quality through expert evaluation. We converted the data of 11,797 sleep studies into CDM and added 632,841 measurements and 9,535 observations to the existing CDM database. Among 86 PSG parameters, 20 were mapped to CDM standard vocabulary and 66 could not be mapped; thus, new custom standard concepts were created. We validated the conversion and usefulness of PSG data through patient-level prediction analyses for the CDM data. We believe that this study represents the first CDM conversion of PSG. In the future, CDM transformation will enable network research in sleep medicine and will contribute to presenting more relevant clinical evidence.
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12
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Duszynski KM, Stark JH, Cohet C, Huang WT, Shin JY, Lai ECC, Man KKC, Choi NK, Khromava A, Kimura T, Huang K, Watcharathanakij S, Kochhar S, Chen RT, Pratt NL. Suitability of databases in the Asia-Pacific for collaborative monitoring of vaccine safety. Pharmacoepidemiol Drug Saf 2021; 30:843-857. [PMID: 33634545 DOI: 10.1002/pds.5214] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 02/22/2021] [Indexed: 11/12/2022]
Abstract
INTRODUCTION Information regarding availability of electronic healthcare databases in the Asia-Pacific region is critical for planning vaccine safety assessments particularly, as COVID-19 vaccines are introduced. This study aimed to identify data sources in the region, potentially suitable for vaccine safety surveillance. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE). METHODS Nineteen countries targeted for database reporting were identified using published country lists and review articles. Surveillance capacity was assessed using two surveys: a 9-item introductory survey and a 51-item full survey. Survey questions related to database characteristics, covariate and health outcome variables, vaccine exposure characteristics, access and governance, and dataset linkage capability. Other questions collated research/regulatory applications of the data and local publications detailing database use for research. RESULTS Eleven databases containing vaccine-specific information were identified across 8 countries. Databases were largely national in coverage (8/11, 73%), encompassed all ages (9/11, 82%) with population size from 1.4 to 52 million persons. Vaccine exposure information varied particularly for standardized vaccine codes (5/11, 46%), brand (7/11, 64%) and manufacturer (5/11, 46%). Outcome data were integrated with vaccine data in 6 (55%) databases and available via linkage in 5 (46%) databases. Data approval processes varied, impacting on timeliness of data access. CONCLUSIONS Variation in vaccine data availability, complexities in data access including, governance and data release approval procedures, together with requirement for data linkage for outcome information, all contribute to the challenges in building a distributed network for vaccine safety assessment in the Asia-Pacific and globally. Common data models (CDMs) may help expedite vaccine safety research across the region.
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Affiliation(s)
- Katherine M Duszynski
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
| | - James H Stark
- Vaccine Medical, Scientific and Clinical Affairs, Pfizer Inc., New York, New York, USA
| | - Catherine Cohet
- Vaccines Clinical Research & Development, GlaxoSmithKline, Wavre, Belgium
| | - Wan-Ting Huang
- Office of Preventive Medicine, Taiwan Centers for Disease Control, Taipei, Taiwan
| | - Ju-Young Shin
- School of Pharmacy, Sungkyunkwan University, Suwon, South Korea
| | - Edward Chia-Cheng Lai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Kenneth K C Man
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK.,Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong
| | - Nam-Kyong Choi
- Department of Health Convergence, Ewha Womans University, Seoul, South Korea
| | - Alena Khromava
- Epidemiology and Benefit Risk, Sanofi Pasteur Ltd., Toronto, Ontario, Canada
| | | | - Kui Huang
- Global Medical Epidemiology, Worldwide Medical and Safety, Pfizer Inc., New York, New York, United States of America
| | | | - Sonali Kochhar
- Global Healthcare Consulting, New Delhi, India.,Department of Global Health, University of Washington, Seattle, Washington, USA
| | - Robert T Chen
- Brighton Collaboration, The Task Force for Global Health, Decatur, Georgia, USA
| | - Nicole L Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
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Kent S, Burn E, Dawoud D, Jonsson P, Østby JT, Hughes N, Rijnbeek P, Bouvy JC. Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment. PHARMACOECONOMICS 2021; 39:275-285. [PMID: 33336320 PMCID: PMC7746423 DOI: 10.1007/s40273-020-00981-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/05/2020] [Indexed: 05/28/2023]
Abstract
There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making.
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Affiliation(s)
- Seamus Kent
- National Institute for Health and Care Excellence, London, United Kingdom
| | - Edward Burn
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Dalia Dawoud
- National Institute for Health and Care Excellence, London, United Kingdom
| | - Pall Jonsson
- National Institute for Health and Care Excellence, London, United Kingdom
| | | | - Nigel Hughes
- Janssen Research and Development, Beerse, Belgium
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jacoline C Bouvy
- National Institute for Health and Care Excellence, London, United Kingdom.
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14
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Su CC, Chia-Cheng Lai E, Kao Yang YH, Man KKC, Kubota K, Stang P, Schuemie M, Ryan P, Hardy C, Zhang Y, Kimura S, Kamijima Y, Wong ICK, Setoguchi S. Incidence, prevalence and prescription patterns of antipsychotic medications use in Asia and US: A cross-nation comparison with common data model. J Psychiatr Res 2020; 131:77-84. [PMID: 32947205 DOI: 10.1016/j.jpsychires.2020.08.025] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 08/20/2020] [Accepted: 08/22/2020] [Indexed: 11/17/2022]
Abstract
The use of antipsychotic medications (APMs) could be different among countries due to availability, approved indications, characteristics and clinical practice. However, there is limited literature providing comparisons of APMs use among countries. To examine trends in antipsychotic prescribing in Taiwan, Hong Kong, Japan, and the United States, we conducted a cross-national study from 2002 to 2014 b y using the distributed network approach with common data model. We included all patients who had at least a record of antipsychotic prescription in this study, and defined patients without previous exposure of antipsychotics for 6 months before the index date as new users for incidence estimation. We calculated the incidence, prevalence, and prescription rate of each medication by calendar year. Among older patients, sulpiride was the most incident [incidence rate (IR) 11.0-23.3) and prevalent [prevalence rate (PR) 11.9-14.3) APM in Taiwan, and most prevalent (PR 2.5-3.9) in Japan. Quetiapine and haloperidol were most common in the United States (IR 8.1-9.5; PR 18.0-18.4) and Hong Kong (PR 8.8-13.7; PR 10.6-12.7), respectively. The trend of quetiapine use was increasing in Taiwan, Hong Kong and the United States. As compared to older patients, the younger patients had more propensity to be prescribed second-generation APM for treatment in four countries. Trends in antipsychotic prescribing varied among countries. Quetiapine use was most prevalent in the United States and increasing in Taiwan and Hong Kong. The increasing use of quetiapine in the elderly patients might be due to its safety profile compared to other APMs.
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Affiliation(s)
- Chien-Chou Su
- Department of Pharmacy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; School of Pharmacy And, College of Medicine National Cheng Kung University, Tainan, Taiwan; Health Outcome Research Center, National Cheng Kung University, Tainan, Taiwan
| | - Edward Chia-Cheng Lai
- Department of Pharmacy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; School of Pharmacy And, College of Medicine National Cheng Kung University, Tainan, Taiwan; Health Outcome Research Center, National Cheng Kung University, Tainan, Taiwan
| | - Yea-Huei Kao Yang
- Department of Pharmacy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; School of Pharmacy And, College of Medicine National Cheng Kung University, Tainan, Taiwan; Health Outcome Research Center, National Cheng Kung University, Tainan, Taiwan.
| | - Kenneth K C Man
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, University of Hong Kong, Hong Kong; Research Department of Practice and Policy, UCL School of Pharmacy, London, United Kingdom
| | | | - Paul Stang
- Janssen Research & Development, LLC, Titusville, United States
| | | | - Patrick Ryan
- Janssen Research & Development, LLC, Titusville, United States
| | | | | | | | | | - Ian C K Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, University of Hong Kong, Hong Kong; Research Department of Practice and Policy, UCL School of Pharmacy, London, United Kingdom
| | - Soko Setoguchi
- Department of Medicine, Rutgers Robertood Johnson Medical School and Institute for Health, Health Care Policy and Aging Research, Rutgers Biomedical and Health Sciences, New Jersey, United States
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15
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Sung SF, Hsieh CY, Hu YH. Two Decades of Research Using Taiwan's National Health Insurance Claims Data: Bibliometric and Text Mining Analysis on PubMed. J Med Internet Res 2020; 22:e18457. [PMID: 32543443 PMCID: PMC7327589 DOI: 10.2196/18457] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/12/2020] [Accepted: 04/16/2020] [Indexed: 12/18/2022] Open
Abstract
Background Studies using Taiwan’s National Health Insurance (NHI) claims data have expanded rapidly both in quantity and quality during the first decade following the first study published in 2000. However, some of these studies were criticized for being merely data-dredging studies rather than hypothesis-driven. In addition, the use of claims data without the explicit authorization from individual patients has incurred litigation. Objective This study aimed to investigate whether the research output during the second decade after the release of the NHI claims database continues growing, to explore how the emergence of open access mega journals (OAMJs) and lawsuit against the use of this database affect the research topics and publication volume and to discuss the underlying reasons. Methods PubMed was used to locate publications based on NHI claims data between 1996 and 2017. Concept extraction using MetaMap was employed to mine research topics from article titles. Research trends were analyzed from various aspects, including publication amount, journals, research topics and types, and cooperation between authors. Results A total of 4473 articles were identified. A rapid growth in publications was witnessed from 2000 to 2015, followed by a plateau. Diabetes, stroke, and dementia were the top 3 most popular research topics whereas statin therapy, metformin, and Chinese herbal medicine were the most investigated interventions. Approximately one-third of the articles were published in open access journals. Studies with two or more medical conditions, but without any intervention, were the most common study type. Studies of this type tended to be contributed by prolific authors and published in OAMJs. Conclusions The growth in publication volume during the second decade after the release of the NHI claims database was different from that during the first decade. OAMJs appeared to provide fertile soil for the rapid growth of research based on NHI claims data, in particular for those studies with two or medical conditions in the article title. A halt in the growth of publication volume was observed after the use of NHI claims data for research purposes had been restricted in response to legal controversy. More efforts are needed to improve the impact of knowledge gained from NHI claims data on medical decisions and policy making.
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Affiliation(s)
- Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan.,Department of Information Management, Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan
| | - Cheng-Yang Hsieh
- Department of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan.,School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ya-Han Hu
- Department of Information Management, National Central University, Taoyuan City, Taiwan.,Center for Innovative Research on Aging Society, National Chung Cheng University, Chiayi County, Taiwan.,MOST AI Biomedical Research Center, National Cheng Kung University, Tainan, Taiwan
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16
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Li R, Chen Y, Ritchie MD, Moore JH. Electronic health records and polygenic risk scores for predicting disease risk. Nat Rev Genet 2020; 21:493-502. [PMID: 32235907 DOI: 10.1038/s41576-020-0224-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2020] [Indexed: 01/03/2023]
Abstract
Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.
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Affiliation(s)
- Ruowang Li
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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17
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Using clinical registries, administrative data and electronic medical records to improve medication safety and effectiveness in dementia. Curr Opin Psychiatry 2020; 33:163-169. [PMID: 31972590 DOI: 10.1097/yco.0000000000000579] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
PURPOSE OF REVIEW Clinical registries, routinely collected administrative data and electronic medical records (EMRs) provide new opportunities to investigate medication safety and effectiveness. This review outlines the strengths and limitations of these data, and highlights recent research related to safe and effective medication use in dementia. RECENT FINDINGS Clinical registries, administrative data and EMRs facilitate observational research among people often excluded from randomized controlled trials (RCTs). Larger sample sizes and longer follow-up times permit research into less common adverse events not apparent in RCTs. The validity of diagnoses recorded in administrative data and EMRs remains variable, although positive predictive values are typically high and sensitivity is low. Dispensing records are a rich source of data for estimating medication exposure. Recent research has investigated medications and prescribing patterns as risk factors for incident dementia, strategies to alleviate behavioural symptoms and the management of comorbidity. Common study protocols and common data models are examples of distributed network approaches increasingly used to conduct large and generalizable multi-database studies across different countries. SUMMARY Greater availability of electronic health data provides important opportunities to address evidence-practice gaps in relation to medication use and safety in people with dementia.
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18
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Brauer R, Wong ICK, Man KK, Pratt NL, Park RW, Cho SY, Li YCJ, Iqbal U, Nguyen PAA, Schuemie M. Application of a Common Data Model (CDM) to rank the paediatric user and prescription prevalence of 15 different drug classes in South Korea, Hong Kong, Taiwan, Japan and Australia: an observational, descriptive study. BMJ Open 2020; 10:e032426. [PMID: 31937652 PMCID: PMC7044847 DOI: 10.1136/bmjopen-2019-032426] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE To measure the paediatric user and prescription prevalence in inpatient and ambulatory settings in South Korea, Hong Kong, Taiwan, Japan and Australia by age and gender. A further objective was to list the most commonly used drugs per drug class, per country. DESIGN AND SETTING Hospital inpatient and insurance paediatric healthcare data from the following databases were used to conduct this descriptive drug utilisation study: (i) the South Korean Ajou University School of Medicine database; (ii) the Hong Kong Clinical Data Analysis and Reporting System; (iii) the Japan Medical Data Center; (iv) Taiwan's National Health Insurance Research Database and (v) the Australian Pharmaceutical Benefits Scheme. Country-specific data were transformed into the Observational Medical Outcomes Partnership Common Data Model. PATIENTS Children (≤18 years) with at least 1 day of observation in any of the respective databases from January 2009 until December 2013 were included. MAIN OUTCOME MEASURES For each drug class, we assessed the per-protocol overall user and prescription prevalence rates (per 1000 persons) per country and setting. RESULTS Our study population comprised 1 574 524 children (52.9% male). The highest proportion of dispensings was recorded in the youngest age category (<2 years) for inpatients (45.1%) with a relatively high user prevalence of analgesics and antibiotics. Adrenergics, antihistamines, mucolytics and corticosteroids were used in 10%-15% of patients. For ambulatory patients, the highest proportion of dispensings was recorded in the middle age category (2-11 years, 67.1%) with antibiotics the most dispensed drug overall. CONCLUSIONS Country-specific paediatric drug utilisation patterns were described, ranked and compared between four East Asian countries and Australia. The widespread use of mucolytics in East Asia warrants further investigation.
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Affiliation(s)
- Ruth Brauer
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
| | - Ian Chi Kei Wong
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, University of Hong Kong, Hong Kong, Hong Kong
| | - Kenneth Kc Man
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, University of Hong Kong, Hong Kong, Hong Kong
| | - Nicole L Pratt
- Quality Use of Medicines and Pharmacy Research Centre, University of South Australia, Adelaide, South Australia, Australia
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Soo-Yeon Cho
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Yu-Chuan Jack Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taiwan Medical University, Taipei, Taiwan
- The International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
- Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan
- International Medical Informatics Association (IMIA), Geneva, Switzerland
| | - Usman Iqbal
- The International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
- Masters & PhD Program in Global Health Department, College of Public Health, Taipei Medical University, Taipei, Taiwan
- Department of Public Health and Community Medicine, Shaikh Khalifa Bin Zayed Al-Nahyan Medical College, Shaikh Zayed Medical Complex, Lahore, Pakistan
| | - Phung-Anh Alex Nguyen
- The International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
| | - Martijn Schuemie
- Epidemiology Analytics, Janssen Research and Development, Titusville, New Jersey, USA
- Department of Biostatistics, University of California, Los Angeles, California, USA
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19
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Ilomäki J, Bell JS, Chan AYL, Tolppanen AM, Luo H, Wei L, Lai ECC, Shin JY, De Paoli G, Pajouheshnia R, Ho FK, Reynolds L, Lau KK, Crystal S, Lau WCY, Man KKC, Brauer R, Chan EW, Shen CY, Kim JH, Lum TYS, Hartikainen S, Koponen M, Rooke E, Bazelier M, Klungel O, Setoguchi S, Pell JP, Cook S, Wong ICK. Application of Healthcare 'Big Data' in CNS Drug Research: The Example of the Neurological and mental health Global Epidemiology Network (NeuroGEN). CNS Drugs 2020; 34:897-913. [PMID: 32572794 PMCID: PMC7306570 DOI: 10.1007/s40263-020-00742-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Neurological and psychiatric (mental health) disorders have a large impact on health burden globally. Cognitive disorders (including dementia) and stroke are leading causes of disability. Mental health disorders, including depression, contribute up to one-third of total years lived with disability. The Neurological and mental health Global Epidemiology Network (NeuroGEN) is an international multi-database network that harnesses administrative and electronic medical records from Australia, Asia, Europe and North America. Using these databases NeuroGEN will investigate medication use and health outcomes in neurological and mental health disorders. A key objective of NeuroGEN is to facilitate high-quality observational studies to address evidence-practice gaps where randomized controlled trials do not provide sufficient information on medication benefits and risks that is specific to vulnerable population groups. International multi-database research facilitates comparisons across geographical areas and jurisdictions, increases statistical power to investigate small subpopulations or rare outcomes, permits early post-approval assessment of safety and effectiveness, and increases generalisability of results. Through bringing together international researchers in pharmacoepidemiology, NeuroGEN has the potential to be paradigm-changing for observational research to inform evidence-based prescribing. The first focus of NeuroGEN will be to address evidence-gaps in the treatment of chronic comorbidities in people with dementia.
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Affiliation(s)
- Jenni Ilomäki
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, VIC, Australia.
| | - J. Simon Bell
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, VIC Australia ,School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Adrienne Y. L. Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | | | - Hao Luo
- Department of Social Work and Social Administration and Sau Po Centre on Ageing, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Li Wei
- Research Department of Practice and Policy, University College London School of Pharmacy, London, UK
| | - Edward Chia-Cheng Lai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ju-Young Shin
- School of Pharmacy, Sungkyunkwan University, Suwon, Gyeong gi-do South Korea
| | - Giorgia De Paoli
- Medicines Monitoring Unit, Ninewells Hospital, School of Medicine, University of Dundee, Dundee, UK
| | - Romin Pajouheshnia
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute of Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands
| | - Frederick K. Ho
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Lorenna Reynolds
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, VIC Australia
| | - Kui Kai Lau
- Department of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Stephen Crystal
- Center for Health Services Research, Institute for Health, Health Care Policy, and Aging Research, Rutgers University, New Brunswick, NJ USA
| | - Wallis C. Y. Lau
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Pokfulam, Hong Kong SAR ,Research Department of Practice and Policy, University College London School of Pharmacy, London, UK
| | - Kenneth K. C. Man
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Pokfulam, Hong Kong SAR ,Research Department of Practice and Policy, University College London School of Pharmacy, London, UK
| | - Ruth Brauer
- Research Department of Practice and Policy, University College London School of Pharmacy, London, UK
| | - Esther W. Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Chin-Yao Shen
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ju Hwan Kim
- School of Pharmacy, Sungkyunkwan University, Suwon, Gyeong gi-do South Korea
| | - Terry Y. S. Lum
- Department of Social Work and Social Administration and Sau Po Centre on Ageing, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | | | - Marjaana Koponen
- School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Evelien Rooke
- Medicines Monitoring Unit, Ninewells Hospital, School of Medicine, University of Dundee, Dundee, UK
| | - Marloes Bazelier
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute of Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands
| | - Olaf Klungel
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute of Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands
| | - Soko Setoguchi
- Rutgers Robert Wood Johnson Medical School and School of Public Health and Center for Pharmacoepidemiology and Treatment Sciences, Institute for Health, Rutgers University, New Brunswick, NJ USA
| | - Jill P. Pell
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Sharon Cook
- Center for Health Services Research, Institute for Health, Health Care Policy, and Aging Research, Rutgers University, New Brunswick, NJ USA
| | - Ian C. K. Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Pokfulam, Hong Kong SAR ,Research Department of Practice and Policy, University College London School of Pharmacy, London, UK
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20
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Noel Nikiema J, Bodenreider O. Comparing the representation of medicinal products in RxNorm and SNOMED CT - Consequences on interoperability. CEUR WORKSHOP PROCEEDINGS 2019; 2931:F1-F6. [PMID: 36276234 PMCID: PMC9584356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Objectives To compare the representation of medicinal products in RxNorm and SNOMED CT and assess the consequences on interoperability. Methods To compare the two models, we manually establish equivalences between the types and definitional features of medicinal products entities in RxNorm and SNOMED CT. We highlight their similarities and differences. Results Both models share major definitional features including ingredient (or substance), strength and dose form. SNOMED CT is more rigorous and better aligned with international standards. In contrast, RxNorm contains implicit knowledge, simplifications and ambiguities, but its model is simpler. Conclusions Since their models are largely compatible, medicinal products from RxNorm and SNOMED CT are expected to be interoperable. However, specific aspects of the alignment between the two models require particular attention.
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Affiliation(s)
- Jean Noel Nikiema
- Bordeaux Population Health Research Center, ERIAS, Univ. Bordeaux, Inserm UMR 1219, F-33000, Bordeaux, France
| | - Olivier Bodenreider
- U.S. National Library of Medicine National Institutes of Health Bethesda, Maryland, USA
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21
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Hsieh CY, Su CC, Shao SC, Sung SF, Lin SJ, Kao Yang YH, Lai ECC. Taiwan's National Health Insurance Research Database: past and future. Clin Epidemiol 2019. [PMID: 31118821 DOI: 10.2147/clep.s196293.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Taiwan's National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan's Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.
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Affiliation(s)
- Cheng-Yang Hsieh
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan
| | - Chien-Chou Su
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Shih-Chieh Shao
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Pharmacy, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan.,Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan
| | - Swu-Jane Lin
- Department of Pharmacy Systems, Outcomes & Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA
| | - Yea-Huei Kao Yang
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Edward Chia-Cheng Lai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan
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22
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Schuemie MJ, Madigan D, Ryan PB, Reich C, Suchard MA, Berlin JA, Hripcsak G. Comment on "How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias". Pharmacoepidemiol Drug Saf 2019; 28:1032-1033. [PMID: 31066478 DOI: 10.1002/pds.4798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 04/17/2019] [Indexed: 11/07/2022]
Affiliation(s)
- Martijn J Schuemie
- Observational Health Data Sciences and Informatics, New York, NY.,Epidemiology Analytics, Janssen Research and Development, Titusville, NJ.,Department of Biostatistics, University of California, Los Angeles, CA
| | - David Madigan
- Observational Health Data Sciences and Informatics, New York, NY.,Department of Statistics, Columbia University, New York, NY
| | - Patrick B Ryan
- Observational Health Data Sciences and Informatics, New York, NY.,Epidemiology Analytics, Janssen Research and Development, Titusville, NJ.,Department of Biomedical Informatics, Columbia University Medical Center, New York, NY
| | - Christian Reich
- Observational Health Data Sciences and Informatics, New York, NY.,Real World Analytics Solutions, IQVIA, Cambridge, MA
| | - Marc A Suchard
- Observational Health Data Sciences and Informatics, New York, NY.,Department of Biostatistics, University of California, Los Angeles, CA.,Department of Biomathematics, University of California, Los Angeles, CA.,Department of Human Genetics, University of California, Los Angeles, CA
| | - Jesse A Berlin
- Observational Health Data Sciences and Informatics, New York, NY.,Epidemiology Analytics, Janssen Research and Development, Titusville, NJ
| | - George Hripcsak
- Observational Health Data Sciences and Informatics, New York, NY.,Department of Biomedical Informatics, Columbia University Medical Center, New York, NY.,Medical Informatics Services, New York-Presbyterian Hospital, New York, NY
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23
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Hsieh CY, Su CC, Shao SC, Sung SF, Lin SJ, Kao Yang YH, Lai ECC. Taiwan's National Health Insurance Research Database: past and future. Clin Epidemiol 2019; 11:349-358. [PMID: 31118821 PMCID: PMC6509937 DOI: 10.2147/clep.s196293] [Citation(s) in RCA: 694] [Impact Index Per Article: 138.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 03/12/2019] [Indexed: 01/29/2023] Open
Abstract
Taiwan’s National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan’s Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.
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Affiliation(s)
- Cheng-Yang Hsieh
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan
| | - Chien-Chou Su
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Shih-Chieh Shao
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Pharmacy, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan.,Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan
| | - Swu-Jane Lin
- Department of Pharmacy Systems, Outcomes & Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA
| | - Yea-Huei Kao Yang
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Edward Chia-Cheng Lai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan
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