| アブストラクト | Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93-100%), while drug-based components were the main contributors in RLDs (81-100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies. |
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| 投稿者 | Roberto, Giuseppe; Leal, Ingrid; Sattar, Naveed; Loomis, A Katrina; Avillach, Paul; Egger, Peter; van Wijngaarden, Rients; Ansell, David; Reisberg, Sulev; Tammesoo, Mari-Liis; Alavere, Helene; Pasqua, Alessandro; Pedersen, Lars; Cunningham, James; Tramontan, Lara; Mayer, Miguel A; Herings, Ron; Coloma, Preciosa; Lapi, Francesco; Sturkenboom, Miriam; van der Lei, Johan; Schuemie, Martijn J; Rijnbeek, Peter; Gini, Rosa |
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| 組織名 | Regional Agency for Healthcare Services of Tuscany, Epidemiology unit, Florence,;Italy.;Department of Medical Informatics, Erasmus University Medical Center, Rotterdam,;Netherlands.;British Heart Foundation Glasgow Cardiovascular Research Centre, University of;Glasgow, Glasgow, United Kingdom.;Pfizer Worldwide Research and Development, Groton, Connecticut, United States of;America.;Department of Biomedical Informatics, Harvard Medical School & Children's;Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts,;United States of America.;GlaxoSmithKline, Worldwide Epidemiology GSK, Stockley Park West, Uxbridge, United;Kingdom.;PHARMO Institute for Drug Outcomes Research, Utrecht, Netherlands.;The Health Improvement Network, Cegedim Strategic Data Medical Research Ltd,;London, United Kingdom.;Quretec, Software Technology and Applications Competence Center, University of;Tartu, Tartu, Estonia.;Estonian Genome Center, University of Tartu, Tartu, Estonia.;Tartu University Hospital, Tartu, Estonia.;Health Search, Italian College of General Practitioners and Primary Care,;Firenze, Italy.;Department of Clinical Epidemiology, Aarhus University Hosptial, Aarhus, Denmark.;University of Manchester, Manchester, United Kingdom.;Arsenal.IT Consortium, Veneto's Research Centre for eHealth Innovation, Treviso,;Hospital del Mar Medical Research Institute (IMIM) and Universitat Pompeu Fabra,;Barcelona, Spain.;Janssen Research & Development, Epidemiology, Titusville, New Jersey, United;States of America.;Observational Health Data Sciences and Informatics, New York, New York, United |
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