Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project

• Published in: PloS one Vol. 11; no. 8; p. e0160648
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 31.08.2016; Public Library of Science (PLoS)
• Subjects: Age; Algorithms; Biology and Life Sciences; Building components; Causes of; Chronic illnesses; Collaboration; Data Mining - methods; Data sources; Databases, Factual; Derivation; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - epidemiology; Diabetis; Diagnostic systems; Documentation; Drugs; Electronic health records; Epidemiology; Europe - epidemiology; Female; Genomes; Health care; Health informatics; Heart; Heterogeneity; Hospitals; Humans; Information systems; Male; Medical diagnosis; Medical informatics; Medical records; Medical research; Medical screening; Medicine and Health Sciences; Mortality; Physical Sciences; Physicians; Primary care; Protocols clínics; Research and Analysis Methods; Source studies; Standard data; Type 2 diabetes; United States; New York; Netherlands; United Kingdom > UK; United States > US; Denmark; Italy; Aarhus Denmark; Estonia; Spain

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...