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

SUBMITTER: Roberto G 

PROVIDER: S-EPMC5006970 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature