title: [Linkage of large secondary and registry data sources with data of cohort studies : usage of a dual potential].
abstract: Cohort studies provide the best evidence of all epidemiological observational studies for the identification of causal relationships between risk factors and diseases. However, this design may lead to drawbacks that may affect the validity and reliability of the results. This follows in particular from systematic errors, such as selection bias or recall bias. One possibility to avoid or counteract some of these drawbacks is to link primary data from cohort studies with secondary and register data. The linkage of these data may also be used for mutual validations. Data that were previously linked with primary data within the context of cohort studies in Germany were obtained from statutory health insurances and pensions as well as data from the Federal Employment Agency and cancer registries. All these data have two features in common: First, they all cover detailed information about a large population and over a long period of time. Second, all sources are in principle able to provide data on an individual level such that an individual data linkage, e.g. with primary data, is possible. However, use and linkage of each of these data sources are restricted by several limitations. These have to be accounted for as well as numerous legal restrictions that exist in Germany to especially prevent the misuse of social data.
authors.affiliation: Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS Bremen, Achterstraße 30, 28359, Bremen, Deutschland. sjacobs@bips.uni-bremen.de.
authors.forename: Svenja
authors.initials: S
authors.lastname: Jacobs
authors.affiliation: Institut für Sozialmedizin und Gesundheitsökonomie, Medizinische Fakultät, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Deutschland.
authors.forename: Christoph
authors.initials: C
authors.lastname: Stallmann
authors.affiliation: Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS Bremen, Achterstraße 30, 28359, Bremen, Deutschland.
authors.forename: Iris
authors.initials: I
authors.lastname: Pigeot
mesh_terms: D002908:Chronic Disease / Q000453:epidemiology*; D015331:Cohort Studies; D057225:Data Mining / Q000379:methods; D066264:Datasets as Topic / Q000706:statistics & numerical data*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005858:Germany / Q000453:epidemiology; D006302:Health Services Research / Q000379:methods; D006801:Humans; D008498:Medical Record Linkage / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D012042:Registries / Q000706:statistics & numerical data*; D012307:Risk Factors