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The data linkage of different data sources for research purposes is being increasingly used in recent years. However, generally accepted methodological guidance is missing. The aim of this article is to provide methodological guidelines and recommendations for research projects that have been consented to across different German research societies. Another aim is to endow readers with a checklist for the critical appraisal of research proposals and articles. This Good Practice Data Linkage (GPD) was already published in German in 2019, but the aspects mentioned can easily be transferred to an international context, especially for other European Union (EU) member states. Therefore, it is now also published in English. Since 2016, an expert panel of members of different German scientific societies have worked together and developed seven guidelines with a total of 27 practical recommendations. These recommendations include (1) the research objectives, research questions, data sources, and resources, (2) the data infrastructure and data flow, (3) data protection, (4) ethics, (5) the key variables and linkage methods, (6) data validation/quality assurance, and (7) the long-term use of data for questions still to be determined. The authors provide a rationale for each recommendation. Future revisions will include new developments in science and updates of data privacy regulations.

Hintergrund: Seit Mai 2018 arbeiten 23 Forschende der Arbeitsgruppe Erhebung und Nutzung von Sekundärdaten (AGENS) der Deutschen Gesellschaft für Sozialmedizin und Prävention e.V. (DGSMP) und der Deutschen Gesellschaft für Epidemiologie e.V. (DGEpi), der Arbeitsgruppe Validierung[zum vollständigen Text gelangen Sie über die oben angegebene URL], 18. Deutscher Kongress für Versorgungsforschung (DKVF)

Das personenbezogene Verknüpfen verschiedener Datenquellen (Datenlinkage) für Forschungszwecke findet in den letzten Jahren in Deutschland zunehmend Anwendung. Jedoch fehlen hierfür konsentierte methodische Standards. Ziel dieses Beitrages ist es, solche Standards für Forschungsvorhaben zu definieren. Eine weitere Intention ist es, dem Lesenden eine Checkliste zur Bewertung geplanter Forschungsvorhaben und Artikel bereitzustellen. Zu diesem Zweck hat eine aus Mitgliedern verschiedener Fachgesellschaften zusammengesetzte Expertengruppe seit 2016 insgesamt 7 Leitlinien mit 27 konkreten Empfehlungen erstellt. Die Gute Praxis Datenlinkage beinhaltet die folgenden Leitlinien: (1) Forschungsziele, Fragestellung, Datenquellen und Ressourcen, (2) Dateninfrastruktur und Datenfluss, (3) Datenschutz, (4) Ethik, (5) Schlüsselvariablen und Linkageverfahren, (6) Datenprüfung/Qualitätssicherung sowie (7) Langfristige Datennutzung für noch festzulegende Fragestellungen. Jede Leitlinie wird ausführlich diskutiert. Zukünftige Aktualisierungen werden wissenschaftliche und datenschutzrechtliche Entwicklungen berücksichtigen.

Additional file 2. Most frequently observed comorbidities and disease-associated symptoms (3-digit ICD 10 level) in patients with prevalent HD in 2015 and 2016

OBJECTIVE: Claims data need to be validated to assess their use for epidemiological research. This study aimed to examine the validity of mortality information in the German Pharmacoepidemiological Research Database (GePaRD). DESIGN: Validation study, secondary data, medical claims. SETTING: Claims data of two German nationwide acting statutory health insurance providers (SHIs) contributing data for GePaRD; record linkage with epidemiological cancer registry providing individual official mortality information. PARTICIPANTS: All women insured with the two SHIs whose insurance coverage ended in the period 2006–2013 and who were residents of North Rhine Westphalia. MEASURES: Descriptive statistics were used to analyse the performance of the linkage procedure. Further, we calculated measures of agreement between the official and the GePaRD-based vital status and assessed differences between the official and the GePaRD-based date of death. RESULTS: Of the 256 111 women of the linkage sample, 25 528 were classified as ‘deceased’ in GePaRD and the others as ‘alive’. Compared with the official data, the GePaRD-based vital status showed a sensitivity of 95.9% and a specificity of 99.4%. The negative predictive value was 99.6% and the positive predictive value 94.3%. The date of death agreed in 96.3% between both data sources. CONCLUSIONS: The vital status recorded in GePaRD was of high accuracy and discrepancies between dates of death in GePaRD and official dates were rare. This underlines the potential of the database for conducting large cohort studies with mortality as the endpoint.

Routine data of statutory health insurance providers (SHI) are increasingly used in health services research. The size, the longitudinal character of the data, the largely unselected populations as well as the possibility to evaluate routine care render SHI routine data an attractive data source for health services research. Since the data is generated for reimbursement processes, SHI routine data is limited with regard to the range and depth of information. Furthermore, the validity of the data for research purposes often is unclear. The aim of this thesis is to discuss the methodological challenges regarding the use of SHI routine data in health services research arising from the limitations inherent in the data and to further outline approaches to overcome these limitations. One of the biggest challenges regarding the use of SHI routine data in health services research is the fact that information on disease severity which may represent a confounder in analytical health services research is largely lacking. In this context, advantages and disadvantages of different propensity score methods regarding their potential for confounder control are discussed in the thesis. A further challenge is related to the fact that studies are often based on data from a single or few SHIs. Since insurants covered by the various SHIs differ with regard to sociodemographic characteristics which, in turn, can be related to the morbidity risk or the probability of receiving certain health services, the external validity of study results might be limited. Concerning this matter, weighting approaches to account for these differences are discussed. To evaluate the validity of SHI routine data with an external gold standard or to enrich SHI routine data with external data due to the limited range of information, a patient-individual record linkage with another data source is required. However, such a record linkage often cannot be realized, since processing person-identifying information requires meeting very high standards of data protection. In this thesis, methods allowing a record linkage of SHI routine data with another data source based on person-identifying information are discussed. Finally, there is an outlook regarding the availability of SHI routine data for research purposes.

Electronic healthcare databases are of increasing importance in health research and mortality is one of the most relevant outcomes. However, data in these databases need to be validated, since they are often generated for reimbursement purposes. The aims of this study were to compare mortality figures from the German Pharmacoepidemiological Research Database (GePaRD) on an aggregated level with external data from the Federal Statistical Office of Germany (FSOG) and to assess consistency of records of death from core data and hospital data within GePaRD.

Einleitung und Fragestellung: Die zunehmende Spezialisierung im ambulanten Sektor führt in vielen Bereichen zur Leistungserbringung auch im Bereich der niedergelassenen spezialärztlichen Behandlung. Ziel der Studie war die Untersuchung des Komplikationsrisikos beim Einsatz von elektiven perkutanen[for full text, please go to the a.m. URL], GMDS 2013; 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Einleitung und Fragestellung: Die Herzinsuffizienz (HI) ist eine der häufigsten, schwerwiegendsten und kostenintensiven Erkrankungen in Industrienationen, welche mit einer hohen Sterblichkeit einhergeht. Abhängig von Krankheitsstadium (New York Heart Association, NYHA I-IV) und Komorbidität[for full text, please go to the a.m. URL], GMDS 2012; 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Einleitung und Fragestellung: Routinedaten der Gesetzlichen Krankenversicherung (GKV) enthalten keine Informationen über die Todesursache von verstorbenen Versicherten. Anhand des Austrittsgrunds aus der Versicherung bzw. des Entlassungsgrunds aus dem Krankenhaus ist lediglich der Tod identifizierbar.[for full text, please go to the a.m. URL], GMDS 2012; 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)