title: Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project.
abstract: 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.
authors: Regional Agency for Healthcare Services of Tuscany, Epidemiology unit, Florence, Italy.
authors: Giuseppe
authors: http://orcid.org/0000-0001-6478-6442
authors: G
authors: Roberto
authors: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
authors: Ingrid
authors: I
authors: Leal
authors: British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom.
authors: Naveed
authors: N
authors: Sattar
authors: Pfizer Worldwide Research and Development, Groton, Connecticut, United States of America.
authors: A Katrina
authors: AK
authors: Loomis
authors: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.|Department of Biomedical Informatics, Harvard Medical School & Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.
authors: Paul
authors: P
authors: Avillach
authors: GlaxoSmithKline, Worldwide Epidemiology GSK, Stockley Park West, Uxbridge, United Kingdom.
authors: Peter
authors: P
authors: Egger
authors: PHARMO Institute for Drug Outcomes Research, Utrecht, Netherlands.
authors: Rients
authors: R
authors: van Wijngaarden
authors: The Health Improvement Network, Cegedim Strategic Data Medical Research Ltd, London, United Kingdom.
authors: David
authors: D
authors: Ansell
authors: Quretec, Software Technology and Applications Competence Center, University of Tartu, Tartu, Estonia.
authors: Sulev
authors: S
authors: Reisberg
authors: Estonian Genome Center, University of Tartu, Tartu, Estonia.|Tartu University Hospital, Tartu, Estonia.
authors: Mari-Liis
authors: ML
authors: Tammesoo
authors: Estonian Genome Center, University of Tartu, Tartu, Estonia.|Tartu University Hospital, Tartu, Estonia.
authors: Helene
authors: H
authors: Alavere
authors: Health Search, Italian College of General Practitioners and Primary Care, Firenze, Italy.
authors: Alessandro
authors: A
authors: Pasqua
authors: Department of Clinical Epidemiology, Aarhus University Hosptial, Aarhus, Denmark.
authors: Lars
authors: L
authors: Pedersen
authors: University of Manchester, Manchester, United Kingdom.
authors: James
authors: J
authors: Cunningham
authors: Arsenàl.IT Consortium, Veneto's Research Centre for eHealth Innovation, Treviso, Italy.
authors: Lara
authors: L
authors: Tramontan
authors: Hospital del Mar Medical Research Institute (IMIM) and Universitat Pompeu Fabra, Barcelona, Spain.
authors: Miguel A
authors: MA
authors: Mayer
authors: PHARMO Institute for Drug Outcomes Research, Utrecht, Netherlands.
authors: Ron
authors: R
authors: Herings
authors: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
authors: Preciosa
authors: P
authors: Coloma
authors: Regional Agency for Healthcare Services of Tuscany, Epidemiology unit, Florence, Italy.
authors: Francesco
authors: F
authors: Lapi
authors: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
authors: Miriam
authors: M
authors: Sturkenboom
authors: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
authors: Johan
authors: J
authors: van der Lei
authors: Janssen Research & Development, Epidemiology, Titusville, New Jersey, United States of America.|Observational Health Data Sciences and Informatics, New York, New York, United States of America.
authors: Martijn J
authors: MJ
authors: Schuemie
authors: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
authors: Peter
authors: P
authors: Rijnbeek
authors: Regional Agency for Healthcare Services of Tuscany, Epidemiology unit, Florence, Italy.
authors: Rosa
authors: R
authors: Gini
mesh_terms: D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D005060:Europe / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male