Anreicherung eines GKV-Datensatzes mit amtlichen Todesursachen über einen Abgleich mit dem Epidemiologischen Krebsregister Nordrhein-Westfalen: Machbarkeitsstudie und Methodenvergleich

Langner, Ingo; Krieg, V.; Heidinger, Oliver; Hense, Hans Werner; Zeeb, Hajo

doi:10.1055/s-0043-124669

Cited by 3 publications

(7 citation statements)

References 11 publications

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“…On the other hand, our linkage sample was supposed to have included all actual cases of death as we included all women with a terminated membership. With regard to record matching, examinations of the linkage performance gave no indication that a technically correct match between different persons could occur 5. Third, on switching from one SHI to another, a person will be represented by a new pseudonym in GePaRD and the new data of that person can therefore not be merged with earlier records.…”

Section: Discussionmentioning

confidence: 99%

“…Data processing and the record linkage procedure have been described in detail elsewhere 5. In brief, GePaRD records had to be de-pseudonymised, a procedure which involved a trusted third party.…”

Section: Methodsmentioning

confidence: 99%

“…The resulting data set was then merged with the original data set. Cases included in the linkage sample with a successful match in the ECR-NRW records were considered verified cases of death 5…”

Section: Methodsmentioning

confidence: 99%

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Individual mortality information in the German Pharmacoepidemiological Research Database (GePaRD): a validation study using a record linkage with a large cancer registry

et al. 2019

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ObjectiveClaims 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).DesignValidation study, secondary data, medical claims.SettingClaims 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.ParticipantsAll women insured with the two SHIs whose insurance coverage ended in the period 2006–2013 and who were residents of North Rhine Westphalia.MeasuresDescriptive 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.ResultsOf 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.ConclusionsThe 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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Individual mortality information in the German Pharmacoepidemiological Research Database (GePaRD): a validation study using a record linkage with a large cancer registry

et al. 2019

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“…The algorithm-based individual assignment of a death by BC in GePaRD was compared with the individual official CoD provided by the State Cancer Registry of North Rhine Westphalia (CR-NRW) that holds information on the official causes of each deceased person in NRW since 2006. This information was linked via a probabilistic record linkage to the claims data on the person level (for details see reference 7). In addition, to assess the performance of the algorithm-based classification on the population level, age-specific BC death rates estimated in the entire GePaRD data base were compared with data from the German Centre for Cancer Registry Data (ZfKD) that provided data on the national BC mortality rates.…”

Section: Methodsmentioning

confidence: 99%

Implementation of an algorithm for the identification of breast cancer deaths in German health insurance claims data: a validation study based on a record linkage with administrative mortality data

et al. 2019

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ObjectiveTo adapt a Canadian algorithm for the identification of female cases of breast cancer (BC) deaths to German health insurance claims data and to test and validate the algorithm by comparing results with official cause of death (CoD) data on the individual and the population level.DesignValidation study, secondary data, medical claims.SettingClaims data of two statutory health insurance providers (SHIs) for inpatient and outpatient care, CoD added via record linkage with epidemiological cancer registry (ECR).ParticipantsAll women insured with the two SHIs and who deceased in the period 2006–2013, were residents of North Rhine Westphalia (NRW) and were linked with ECR data: n=22 413.Main outcome measuresBased on inpatient and outpatient diagnoses in the year before death, six algorithms were derived and the accordance of the algorithm-based CoD with the official CoD was evaluated calculating specificity, sensitivity, negative and positive predictive values (NPV, PPV). Furthermore, algorithm-based age-specific BC mortality rates covering several calendar years were calculated for the entire insured female population and compared with official national rates.ResultsOur final algorithm, derived from the NRW subsample, comprised codes indicating the presence of BC, metastases, a terminal illness phase and the absence of codes for other tumours. Overall, specificity, sensitivity, NPV and PPV of this algorithm were 97.4%, 91.3%, 98.9% and 81.7%, respectively. In the age range 40–80 years, sensitivity and PPV slightly decreased with increasing age. Algorithm-based age-specific BC mortality rates agreed well with official rates except for the age group 85 years and older.ConclusionsThe algorithm-based identification of BC deaths in German claims data is feasible and valid, except for higher ages. The algorithm to ascertain BC mortality rates in an epidemiological study seems applicable when information on the official CoD is not available in the original database.

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“…As the ECR is allowed to handle double encrypted identifiers only [9,10], a procedure is chosen which encrypts the SHI person identifiers corresponding to the requirements of the ECR. The ECR provides the SHI with a data-transfer and encryption software tool, which the SHI employ for the first encryption (MD5 method) of the identifiers and an additional transport encryption [11]. These encrypted data are transferred from the SHIs to the ECR via a specific pseudonymization service (implemented at the Kas-senärztliche Vereinigung Westfalen-Lippe) which removes the transport encryption and applies the second encryption (IDEA method).…”

Section: Description Of the Dataflow For The Record Linkagementioning

confidence: 99%

Linkage of Routine Data to Other Data Sources in Germany: A Practical Example Illustrating Challenges and Solutions

et al. 2019

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ZusammenfassungRoutinedaten haben ein hohes Potenzial für die epidemiologische und Versorgungsforschung, doch fehlen beispielsweise Informationen zu Todesursachen und es mangelt häufig an detaillierten Informationen u. a. zu Lebensstilfaktoren. In Deutschland ist v. a. aufgrund der strengen Datenschutzauflagen eine unmittelbare Ergänzung dieser Informationen durch andere Datenquellen („Linkage“) mit einigen Herausforderungen verbunden. So besteht eine Herausforderung darin, dass die Routinedatennutzer üblicherweise keinen Zugriff auf Personenidentifikatoren besitzen, die für ein Linkage erforderlich sind. Darüber hinaus sollten sensible Informationen (z. B. die Todesursache) nicht an Institutionen übermittelt werden, die selbst über Personenidentifikatoren verfügen. In diesem Artikel veranschaulichen wir diese zentralen Herausforderungen und stellen entsprechende Lösungen anhand eines Praxisbeispiels vor, bei dem die Abrechnungsdaten von gesetzlichen Krankenkassen mit einem epidemiologischen Krebsregister verknüpft werden, um Informationen zur Todesursache zu erhalten. Wir beschreiben die für das Linkage notwendigen Genehmigungsverfahren, den Datenfluss zwischen den beteiligten Institutionen und erläutern die Gründe für den Datenfluss im Hinblick auf die wesentlichen Herausforderungen. Schließlich verallgemeinern wir die Fragen, die bei der Planung eines Linkage-Verfahrens zu klären sind und zeigen mögliche weitere Herausforderungen auf. Mit diesem praktischen Beispiel zeigen wir, dass ein Abgleich zwischen Routinedaten und anderen Datenquellen in Deutschland umsetzbar ist, dabei aber bestimmte Beschränkungen und Hindernisse berücksichtigt werden müssen.

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Anreicherung eines GKV-Datensatzes mit amtlichen Todesursachen über einen Abgleich mit dem Epidemiologischen Krebsregister Nordrhein-Westfalen: Machbarkeitsstudie und Methodenvergleich

Cited by 3 publications

References 11 publications

Individual mortality information in the German Pharmacoepidemiological Research Database (GePaRD): a validation study using a record linkage with a large cancer registry

Individual mortality information in the German Pharmacoepidemiological Research Database (GePaRD): a validation study using a record linkage with a large cancer registry

Implementation of an algorithm for the identification of breast cancer deaths in German health insurance claims data: a validation study based on a record linkage with administrative mortality data

Linkage of Routine Data to Other Data Sources in Germany: A Practical Example Illustrating Challenges and Solutions

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